Code import - branch release/SIENTIAPDE-1645

.dockerignore

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+# ============================================================================
+# WHITELIST APPROACH: Block everything by default, then allow only what's needed
+# ============================================================================
+
+# Block everything first
+*
+
+# ============================================================================
+# ALLOW: Application source code (model_manager package)
+# ============================================================================
+
+# Allow the main package directory and all Python files
+!model_manager/
+!model_manager/**/*.py
+!model_manager/**/__init__.py
+
+# Allow subdirectories structure
+!model_manager/activities/
+!model_manager/activities/**
+!model_manager/schedules/
+!model_manager/schedules/**
+!model_manager/sientia/
+!model_manager/sientia/**
+!model_manager/utils/
+!model_manager/utils/**
+!model_manager/utils/models/
+!model_manager/utils/models/**
+!model_manager/utils/repository/
+!model_manager/utils/repository/**
+!model_manager/worker/
+!model_manager/worker/**
+!model_manager/workflows/
+!model_manager/workflows/**
+
+# Allow reports directory with header.html
+!model_manager/reports/
+!model_manager/reports/header.html
+
+# Allow temp directory structure (but not its contents)
+!model_manager/reports/temp/
+
+# ============================================================================
+# ALLOW: Dependencies file (needed for pip install in Dockerfile)
+# ============================================================================
+!requirements.txt
+
+# ============================================================================
+# BLOCK: Explicitly block unwanted files even if they match above patterns
+# ============================================================================
+
+# Python cache and compiled files
+**/__pycache__/
+**/*.pyc
+**/*.pyo
+**/*.pyd
+**/.Python
+**/*.so
+**/*.egg
+**/*.egg-info/
+
+# Tests (not needed in production)
+model_manager/**/test_*.py
+model_manager/**/*_test.py
+
+# IDE and editor files
+**/.vscode/
+**/.idea/
+**/*.swp
+**/*.swo
+**/*~
+
+# OS files
+**/.DS_Store
+**/Thumbs.db
+
+# Logs and temporary files
+**/*.log
+**/*.tmp
+**/*.temp
+
+# Local configuration
+**/.env
+**/.env.local
+**/*.local
+
+# Documentation inside code
+**/*.md
+**/README*
+
+# Backup files
+**/*.bak
+**/*.backup
+**/*.old

.env.example

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+POSTGRES_HOST=localhost
+POSTGRES_PORT=5432
+POSTGRES_USER=postgres
+POSTGRES_PASSWORD=changeme
+POSTGRES_DBNAME=sientia
+POSTGRES_MIN_CONNECTIONS=10
+POSTGRES_MAX_CONNECTIONS=30
+
+MLFLOW_URL=http://localhost:5080
+MLFLOW_USERNAME=aignosi
+MLFLOW_PASSWORD=changeme
+
+LOG_LEVEL=DEBUG
+HTTP_METRICS_PORT=9090
+HTTP_SDK_METRICS_PORT=9091
+PROJECT_NAME=sientia-model-manager
+
+TEMPORAL_HOST=localhost:7233
+TEMPORAL_NAMESPACE=model-manager
+TEMPORAL_USE_TLS=false
+RUNTIME=single
+
+MONGODB_USERNAME=root
+MONGODB_PASSWORD=changeme
+MONGODB_URL=localhost:27017
+MONGODB_DATABASE=sientia
+MONGODB_TTL_INDEX_HOURS=1
+
+MINIO_ENDPOINT_URL=http://localhost:9000
+MINIO_ACCESS_KEY=minioadmin
+MINIO_SECRET_KEY=minioadmin
+MINIO_DEFAULT_BUCKET=model-training
+MINIO_SECURE=false
+
+STORE_BASE_URL=http://localhost:3000
+STORE_OWNER=aignosi
+STORE_REPO=suse-model-store
+STORE_USERNAME=
+STORE_PASSWORD=
+STORE_CACHE_TTL_SECONDS=3600
+PYPI_SERVER=http://localhost:5000
+PYPI_USERNAME=
+PYPI_PASSWORD=
+
+TIMEOUT_VALIDATE_PARAMS=30
+TIMEOUT_TRAIN_MODEL=2700
+TIMEOUT_DELETE_FILE=120
+TIMEOUT_UPDATE_DATABASE=30
+
+CLEANUP_RETENTION_HOURS=24
+CLEANUP_DRY_RUN=false
+TIMEOUT_CLEANUP_LOCAL=120
+
+CLEANUP_SCHEDULE_ID=cleanup-files-daily
+CLEANUP_CRON="0 0 * * *"
+CLEANUP_TIMEZONE=UTC
+CLEANUP_EXECUTION_TIMEOUT_HOURS=1

.github/workflows/deploy.yml

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+name: Deploy Python Application
+
+on:
+  pull_request:
+    types:
+      - closed
+    branches:
+      - main
+      - 'release/**'
+      - 'feature/**'
+
+jobs:
+  deploy:
+    name: Deploy
+    if: github.event.pull_request.merged == true
+    permissions: write-all
+    uses: Aignosi/github_workflow_templates/.github/workflows/reusable-deploy.yml@main
+    with:
+      branch_name: ${{ github.event.pull_request.head.ref }}
+      project_type: 'python'
+      image_name: 'sientia-dataops-model-manager'
+      helm_chart_path: 'sientia-module'
+      helm_release_name: 'sientia-dataops-model-manager'
+      helm_namespace: 'sientia'
+      update_version_in: '["pyproject", "values"]'
+      app_owner: 'Aignosi'
+      private_repos: 'sientia-dataops-library'
+      helm_repo_name: 'sientia'
+      helm_repo_url: 'https://raw.githubusercontent.com/Aignosi/sientia-dataops-helm-repo/refs/heads/main/'
+      helm_chart_version: '0.6.0'
+      helm_values_file: './values.yaml'
+      use_vpn: true
+    secrets: inherit

.github/workflows/quality-gate.yml

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+name: Quality gate
+
+on:
+  pull_request:
+    branches:
+      - main
+      - 'release/**'
+      - 'feature/**'
+    types: [ opened, synchronize, reopened ]
+
+jobs:
+  quality-gate:
+    uses: Aignosi/github_workflow_templates/.github/workflows/python-quality-gate.yml@main
+    permissions: write-all
+    with:
+      project_name: 'model_manager'
+      repositories: 'sientia-dataops-library,sientia-model-library'
+    secrets: inherit

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py.cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Code quality tools cache
+.bandit/
+validate.txt
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+#poetry.toml
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+#pdm.lock
+#pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+#pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
+.pixi
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.venv
+env/
+venv/
+ENV/
+venv_311/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+
+# Abstra
+# Abstra is an AI-powered process automation framework.
+# Ignore directories containing user credentials, local state, and settings.
+# Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#  Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#  that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#  and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#  you could uncomment the following to ignore the entire vscode folder
+.vscode/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Cursor
+#  Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to
+#  exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
+#  refer to https://docs.cursor.com/context/ignore-files
+.cursorignore
+.cursorindexingignore
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/
+
+# Ignore Docker volumes
+docker-compose.override.yml
+**/db_data/
+**/kafka-volume/
+**/zookeeper-volume/
+**/mage_data/
+**/minio_data/
+**/venv/
+**/certs/*.pem
+**/certs/*.der
+**/certs/*.csr
+**/deploy/*.yaml
+scouter/.file_versions/
+scouter/pipelines/**/triggers.yaml
+**/postgres_data/**
+
+# Ignore Python cache files
+*.pyc
+*.pyo
+*.pyd
+
+# Ignore temporary files
+*.swp
+
+# Ignore test run reports in model_manager/reports/temp (but keep temp folder and .gitkeep)
+model_manager/reports/temp/*
+!model_manager/reports/temp/.gitkeep
+
+# Miscellaneous
+git_key*
+git_log
+tmp/
+sientia-module/
+.secrets
+.event.json
+
+models/
+.cursor
+openspec
+
+# Training smoke test: track JSON inputs, ignore local sample CSVs
+input_dataset.csv
+scripts/**/*.csv
+!scripts/inputs/
+.claude/

Dockerfile

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+# Multi-stage build for optimized Python application
+FROM python:3.11-slim AS builder
+
+# Set build-time environment variables
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1
+
+# Install build dependencies only
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    && rm -rf /var/lib/apt/lists/* && \
+    apt-get clean
+
+# Configure SSH to trust GitHub host key
+RUN mkdir -p ~/.ssh && \
+    ssh-keyscan -t rsa github.com >> ~/.ssh/known_hosts && \
+    chmod 600 ~/.ssh/known_hosts
+
+# Create virtual environment
+RUN python -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# Upgrade pip and wheel for better caching
+RUN pip install --upgrade pip setuptools wheel
+
+# Copy requirements files for better Docker layer caching
+COPY requirements.txt ./
+
+# Install only production dependencies with no cache
+RUN --mount=type=ssh echo "=== Installing dependencies ===" && \
+    pip install --no-cache-dir -r requirements.txt && \
+    echo "=== Dependencies installed successfully ===" && \
+    pip list | wc -l && \
+    echo "=== Cleaning cache files ===" && \
+    find /opt/venv -type d -name __pycache__ -exec rm -rf {} + 2>/dev/null || true && \
+    find /opt/venv -name "*.pyc" -delete 2>/dev/null || true && \
+    rm -rf /root/.cache/pip/* && \
+    echo "=== Cleaning venv site-packages ===" && \
+    find /opt/venv/lib/python3.11/site-packages/ -type f -name "*.md" -delete 2>/dev/null || true && \
+    echo "=== Stripping .so files ===" && \
+    find /opt/venv -name "*.so" -exec strip {} + 2>/dev/null || true
+
+# Production stage using python-slim for better functionality
+FROM python:3.11-slim AS production
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    PATH="/opt/venv/bin:$PATH" \
+    POD_ID=unknown \
+    HOME="/app"
+
+# Copy virtual environment from builder stage
+COPY --from=builder /opt/venv /opt/venv
+
+# Set working directory
+WORKDIR /app
+
+# Copy application code
+COPY . .
+
+# Create necessary directories for runtime file creation
+RUN mkdir -p /app/model_manager/reports /app/logs /app/temp /app/models /app/data && \
+    chmod 755 /app/model_manager/reports /app/logs /app/temp /app/models /app/data
+
+# Create non-root user
+RUN groupadd -r appuser && useradd -r -g appuser appuser && \
+    chown -R appuser:appuser /app
+
+# Switch to non-root user
+USER appuser
+
+# Set entrypoint for proper signal handling and PID 1
+ENTRYPOINT ["/opt/venv/bin/python", "-m", "model_manager.worker.worker"]

LICENSE

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+MIT License
+
+Copyright (c) 2025 aignosi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

PIPELINE_PARAMS_CHANGELOG.md

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+# Changelog de Parâmetros do Pipeline de Treinamento
+
+Este documento descreve as alterações nos parâmetros de entrada do pipeline Temporal para treinamento de modelos.
+
+## Resumo das Alterações
+
+### Parâmetros ALTERADOS (Breaking Changes)
+
+| Parâmetro | Tipo Anterior | Tipo Novo | Descrição |
+|-----------|---------------|-----------|-----------|
+| `lag_train` | `int` | `dict[str, int]` | Agora é um dicionário com lag por variável. Ex: `{"var1": 2, "var2": 3}` |
+| `lag_val` | `int` | `dict[str, int]` | Agora é um dicionário com lag por variável. Ex: `{"var1": 1, "var2": 1}` |
+
+### Parâmetros NOVOS (Obrigatórios)
+
+| Parâmetro | Tipo | Descrição | Valores Válidos |
+|-----------|------|-----------|-----------------|
+| `model_name` | `str` | Nome do tipo de modelo | `"Linear Regression"`, `"Polynomial Regression"` |
+| `degree` | `int` | Grau do polinômio (1 = linear) | `>= 1` |
+| `interaction_only` | `bool` | Apenas termos de interação para polinomial | `true`, `false` |
+| `nan_treatment` | `str` | Tratamento de valores NaN | `"drop"`, `"linear interpolation"`, `"fill linear"` |
+| `scaler_name` | `str` | Nome do scaler a usar | `"Standard Scaler"`, `"None"` |
+
+### Parâmetros NOVOS (Opcionais)
+
+| Parâmetro | Tipo | Descrição | Default |
+|-----------|------|-----------|---------|
+| `start_date` | `str \| null` | Data inicial para filtrar dados (formato: `"YYYY-MM-DD HH:MM:SS"`) | `null` |
+| `end_date` | `str \| null` | Data final para filtrar dados (formato: `"YYYY-MM-DD HH:MM:SS"`) | `null` |
+| `support_filters` | `dict \| null` | Filtros customizados por variável | `{}` |
+| `static_threshold` | `int \| null` | Threshold para remoção de janelas estáticas (1-1000). Só usado quando `rem_static_win` é `true`. | `1` |
+
+---
+
+## Exemplo de Input Completo
+
+### Formato ANTERIOR (não funciona mais):
+
+```json
+{
+  "experiment_run_id": 123,
+  "target_variable": "temperatura",
+  "variable_columns": ["pressao", "umidade", "velocidade"],
+  "lag_train": 2,
+  "lag_val": 1,
+  "rem_static_win": true,
+  "low_lim": {"pressao": 0, "umidade": 0, "velocidade": 0},
+  "upp_lim": {"pressao": 100, "umidade": 100, "velocidade": 50},
+  "window": 0,
+  "use_scaler": true,
+  "include_ar": false,
+  "bucket_name": "training-data",
+  "file_name": "dataset.csv",
+  "line_separator": ";",
+  "decimal_separator": ",",
+  "train_size": 80,
+  "shuffle": false,
+  "experiment_name": "modelo-temperatura",
+  "removed_intervals": []
+}
+```
+
+### Formato NOVO (obrigatório):
+
+```json
+{
+  "experiment_run_id": 123,
+  "target_variable": "temperatura",
+  "variable_columns": ["pressao", "umidade", "velocidade"],
+  
+  "lag_train": {
+    "pressao": 2,
+    "umidade": 2,
+    "velocidade": 2
+  },
+  "lag_val": {
+    "pressao": 1,
+    "umidade": 1,
+    "velocidade": 1
+  },
+  
+  "rem_static_win": true,
+  "static_threshold": null,
+  "low_lim": {"pressao": 0, "umidade": 0, "velocidade": 0},
+  "upp_lim": {"pressao": 100, "umidade": 100, "velocidade": 50},
+  "window": 0,
+  "use_scaler": true,
+  "include_ar": false,
+  "bucket_name": "training-data",
+  "file_name": "dataset.csv",
+  "line_separator": ";",
+  "decimal_separator": ",",
+  "train_size": 80,
+  "shuffle": false,
+  "experiment_name": "modelo-temperatura",
+  "removed_intervals": [],
+  
+  "model_name": "Linear Regression",
+  "degree": 1,
+  "interaction_only": false,
+  "nan_treatment": "drop",
+  "scaler_name": "Standard Scaler",
+  
+  "start_date": null,
+  "end_date": null,
+  "support_filters": {}
+}
+```
+
+---
+
+## Exemplo para Polynomial Regression
+
+```json
+{
+  "experiment_run_id": 124,
+  "target_variable": "temperatura",
+  "variable_columns": ["pressao", "umidade"],
+  
+  "lag_train": {
+    "pressao": 0,
+    "umidade": 0
+  },
+  "lag_val": {
+    "pressao": 0,
+    "umidade": 0
+  },
+  
+  "rem_static_win": false,
+  "low_lim": {"pressao": 0, "umidade": 0},
+  "upp_lim": {"pressao": 100, "umidade": 100},
+  "window": 0,
+  "use_scaler": true,
+  "include_ar": false,
+  "bucket_name": "training-data",
+  "file_name": "dataset.csv",
+  "line_separator": ";",
+  "decimal_separator": ",",
+  "train_size": 80,
+  "shuffle": false,
+  "experiment_name": "modelo-polinomial",
+  "removed_intervals": [],
+  
+  "model_name": "Polynomial Regression",
+  "degree": 2,
+  "interaction_only": false,
+  "nan_treatment": "linear interpolation",
+  "scaler_name": "Standard Scaler",
+  
+  "start_date": "2024-01-01 00:00:00",
+  "end_date": "2024-12-31 23:59:59",
+  "support_filters": {}
+}
+```
+
+---
+
+## Exemplo com Intervalos Removidos
+
+```json
+{
+  "removed_intervals": [
+    ["2024-03-01 00:00:00", "2024-03-15 23:59:59"],
+    ["2024-06-01 00:00:00", "2024-06-30 23:59:59"]
+  ]
+}
+```
+
+---
+
+## Parâmetros Logados no MLflow
+
+Os seguintes parâmetros são agora logados no MLflow:
+
+| Parâmetro MLflow | Descrição |
+|------------------|-----------|
+| `model_name` | Nome do modelo (`Linear Regression` ou `Polynomial Regression`) |
+| `models_params` | `{"degree": int, "interaction_only": bool}` |
+| `target_variable` | Variável alvo |
+| `input_variables` | Lista de variáveis de entrada |
+| `nan_treatment` | Tratamento de NaN |
+| `lag_train` | Dicionário de lags para treino |
+| `lag_transform` | Dicionário de lags para transformação |
+| `static_threshold` | Threshold para janelas estáticas (1 ou null) |
+| `lower_limits` | Limites inferiores por variável |
+| `upper_limits` | Limites superiores por variável |
+| `scaler_name` | Nome do scaler |
+| `scaler_params` | Parâmetros do scaler (mean, variance) |
+| `include_ar` | Se inclui variável autoregressiva |
+| `train_size` | Proporção de treino (0.0 - 1.0) |
+| `test_size` | Proporção de teste (0.0 - 1.0) |
+| `start_date` | Data inicial (ou null) |
+| `end_date` | Data final (ou null) |
+| `removed_intervals` | Lista de intervalos removidos |
+| `retrain` | Sempre `false` para novos modelos |
+| `support_filters` | Filtros customizados |
+
+---
+
+## Validações de Negócio
+
+O sistema valida automaticamente:
+
+1. **`train_size`**: Deve estar entre 10 e 100
+2. **`variable_columns`**: Não pode estar vazio
+3. **`lag_train` / `lag_val`**: Todos os valores devem ser >= 0
+4. **`window`**: Deve ser >= 0
+5. **`degree`**: Deve ser >= 1
+6. **`nan_treatment`**: Deve ser `"drop"`, `"linear interpolation"` ou `"fill linear"`
+7. **`scaler_name`**: Deve ser `"Standard Scaler"` ou `"None"`
+8. **`model_name`**: Deve ser `"Linear Regression"` ou `"Polynomial Regression"`
+9. **`low_lim` / `upp_lim`**: Devem ter as mesmas chaves, e `low_lim[var] < upp_lim[var]`
+10. **`bucket_name` / `file_name` / `experiment_name`**: Não podem estar vazios
+11. **`degree` vs `model_name`**: Se `model_name` = "Polynomial Regression", `degree` deve ser >= 2; se "Linear Regression", `degree` deve ser = 1
+12. **`removed_intervals`**: Cada elemento deve ser lista/tupla com pelo menos 2 elementos (start, end)
+13. **`target_variable`**: Não pode estar vazio
+14. **`static_threshold`**: Se `rem_static_win` = `true` e `static_threshold` tiver valor, deve estar entre 1 e 1000 (inclusive). Se `null`, assume valor `1`.
+
+---
+
+## Arquivos Modificados
+
+- `model_manager/sientia/models.py` - `DataPreprocessor` e `LinearRegressionModel`
+- `model_manager/sientia/metrics.py` - Funções RCE adicionadas
+- `model_manager/utils/models/train_model_params.py` - Novos parâmetros
+- `model_manager/utils/repository/training_repository.py` - Uso dos novos parâmetros
+- `model_manager/utils/repository/model_repository.py` - Logging no MLflow

dashboards/sientia-dataops-model-manager.json

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+    {
+      "id": 7,
+      "title": "Model Quality — R²",
+      "type": "stat",
+      "gridPos": { "h": 4, "w": 8, "x": 16, "y": 16 },
+      "targets": [
+        {
+          "expr": "sientia_training_model_quality_r2",
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+      "targets": [
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+      "type": "stat",
+      "gridPos": { "h": 4, "w": 8, "x": 8, "y": 20 },
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+  "timezone": "browser",
+  "title": "Sientia DataOps Model Manager",
+  "uid": "sientia-dataops-model-manager",
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docs/scenarios.md

@@ -0,0 +1,66 @@
+# E2E Test Scenarios
+
+This document maps the workflow scenarios tested in the E2E suite to their corresponding JSON input files and expected behaviors.
+
+## Infrastructure (second-pass review)
+
+- **Containers:** PostgreSQL, MinIO, MongoDB, and Gitea via testcontainers; real clients and `Activities` code paths.
+- **MLflow:** `file://` tracking URI (real SDK, no remote server).
+- **Temporal:** `WorkflowEnvironment.start_time_skipping()` — official temporalio test runtime; workflows and activities are not stubbed.
+- **Logging/metrics:** `get_logger` + `MetricsController` (sientia_do); no `unittest.mock` for observability in `e2e/conftest.py`.
+- **Unit tests** under `tests/` may still use mocks where appropriate; that policy is separate from this E2E suite.
+
+## 1. TrainModel Workflow (`test_train_model_workflow.py`)
+
+### 1.1 Happy Paths (Successful execution)
+
+| Test Function | Input JSON | Expected Status | Description |
+|---|---|---|---|
+| `test_scenario_1_1_1_linear_regression_basic` | `01-linear-regression-basic.json` | `TRAINING_SUCCESS` | Basic linear regression without scaler. Verifies end-to-end pipeline. |
+| `test_scenario_1_1_2_linear_regression_with_scaler` | `02-linear-regression-with-scaler.json` | `TRAINING_SUCCESS` | Linear regression with `Standard Scaler`. |
+| `test_scenario_1_1_3_polynomial_regression_degree2_with_scaler` | `03-polynomial-regression-degree2.json` | `TRAINING_SUCCESS` | Polynomial regression (degree 2) with Standard Scaler. |
+| `test_scenario_1_1_4_polynomial_regression_degree3_with_scaler` | `04-polynomial-regression-degree3.json` | `TRAINING_SUCCESS` | Polynomial regression (degree 3) with Standard Scaler. |
+| `test_scenario_1_1_5_linear_regression_with_lags` | `05-linear-regression-with-lags.json` | `TRAINING_SUCCESS` | Linear regression with `lag_train`/`lag_val` per variable. |
+| `test_scenario_1_1_6_linear_regression_nan_interpolation` | `06-linear-regression-nan-interpolation.json` | `TRAINING_SUCCESS` | Linear regression with `nan_treatment='linear interpolation'`. |
+| `test_scenario_1_1_7_linear_regression_static_window_removal` | `07-linear-regression-static-window-removal.json` | `TRAINING_SUCCESS` | `rem_static_win=true` with default `static_threshold`. |
+| `test_scenario_1_1_8_linear_regression_with_limits` | `08-linear-regression-with-limits.json` | `TRAINING_SUCCESS` | `support_filters` with `min`/`max` per variable. |
+| `test_scenario_1_1_9_polynomial_degree2_scaler_and_lags` | `09-polynomial-degree2-with-scaler-and-lags.json` | `TRAINING_SUCCESS` | Polynomial (degree 2), Standard Scaler, and lags. |
+| `test_scenario_1_1_10_linear_regression_with_ar_opt_params` | `10-linear-regression-with-ar.json` | `TRAINING_SUCCESS` | `opt_params.include_ar=true` (placeholder for future AR behavior). |
+| `test_scenario_1_1_11_linear_regression_static_threshold_custom` | `11-linear-regression-static-threshold-custom.json` | `TRAINING_SUCCESS` | `rem_static_win=true` with custom `static_threshold`. |
+| `test_scenario_1_1_12_alternate_date_format_dd_mm_yyyy` | `12-angular-test-date-format.json` | `TRAINING_SUCCESS` | `date_column=DATA`, `dd/MM/yyyy` format, object `training_data_dd_mm_yyyy.csv`. |
+| `test_scenario_1_1_13_alternate_csv_narrow_date_window` | `13-angular-test-double-date-column.json` | `TRAINING_SUCCESS` | Same alternate CSV with a bounded `start_date`/`end_date` window. |
+| `test_scenario_1_1_14_polynomial_with_support_filters` | `14-angular-test-polynomial-support-filters.json` | `TRAINING_SUCCESS` | Polynomial (degree 4), scaler, `upper_line`/`lower_line` support filters. |
+| `test_scenario_1_1_15_linear_regression_custom_target_column_name` | `15-linear-regression-custom-target-column.json` | `TRAINING_SUCCESS` | Custom `target_variable` column name (not literal ``target``); Evidently/report columns must match. |
+| `test_scenario_1_1_16_naive_timestamp_header_column` | `16-linear-regression-naive-timestamp-header.json` | `TRAINING_SUCCESS` | `date_column`=`Timestamp`, naive CSV `training_data_timestamp_naive.csv`. |
+| `test_scenario_1_1_17_linear_regression_blank_timestamp_row_dropped` | `17-linear-regression-blank-timestamp-row.json` | `TRAINING_SUCCESS` | One empty timestamp cell; row dropped before index. |
+
+### 1.2 Error Paths
+
+| Test Function | Input JSON | Expected Status | Description |
+|---|---|---|---|
+| `test_scenario_1_2_1_minio_file_not_found` | `01-linear-regression-basic.json` | `TRAINING_ERROR` | MinIO file does not exist. Workflow fails during file download. |
+| `test_scenario_1_2_2_experiment_run_id_not_in_db` | `01-linear-regression-basic.json` | N/A (raises Exception) | `experiment_run_id` does not exist in DB. Workflow fails immediately on status update attempt. |
+
+## 2. Parameter Validation (`test_train_model_validation.py`)
+
+These scenarios test the business rule validations inside `validate_train_params`. All are expected to terminate with `ORCHESTRATOR_VALIDATION_ERROR`.
+
+| Test Function | Modification | Expected Error Substring |
+|---|---|---|
+| `test_scenario_2_1_1_train_size_out_of_range` | `train_size = 5` | `'train_size'` |
+| `test_scenario_2_1_2_empty_variable_columns` | `variable_columns = []` | `'variable_columns'` |
+| `test_scenario_2_1_3_invalid_date_format` | `date_format = 'INVALID'` | `'date_format'` |
+| `test_scenario_2_1_4_whitespace_only_model_name` | `model_name = '   '` | `'model_name'` |
+| `test_scenario_2_1_5_unknown_model_type` | `model_type = 'totally_unknown_model'` | `'totally_unknown_model'` |
+| `test_scenario_2_1_6_missing_target_variable` | `target_variable = ''` | `'target_variable'` |
+| `test_scenario_2_1_7_missing_experiment_run_id` | Missing `experiment_run_id` | N/A (raises ValueError immediately) |
+| `test_scenario_2_1_8_missing_date_column` | Missing `date_column` | N/A (raises ValueError immediately) |
+| `test_scenario_2_1_9_whitespace_date_column` | `date_column = '   '` | `'date_column'` |
+
+## 3. CleanupFiles Workflow (`test_cleanup_files_workflow.py`)
+
+| Test Function | Description |
+|---|---|
+| `test_scenario_3_1_1_cleanup_with_no_temp_dirs` | Temp directory is empty. Activity completes without error. |
+| `test_scenario_3_1_2_cleanup_removes_old_temp_dirs` | Two stale directories matching `name_YYYYMMDD_HHMMSS_microseconds` are removed when older than retention. |
+| `test_scenario_3_1_3_cleanup_nonexistent_temp_path` | Target path does not exist. Handled gracefully without error. |

docs/test-scenarios/01-linear-regression-basic.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Cenário básico de regressão linear sem scaler",
+  "experiment_run_id": 1001,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/02-linear-regression-with-scaler.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão linear com Standard Scaler habilitado",
+  "experiment_run_id": 1002,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "Standard Scaler"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/03-polynomial-regression-degree2.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão polinomial de grau 2 com scaler (obrigatório para evitar overflow)",
+  "experiment_run_id": 1003,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Polynomial Regression",
+  "model_type": "polynomial_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 2,
+    "interaction_only": false,
+    "scaler_name": "Standard Scaler"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/04-polynomial-regression-degree3.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão polinomial de grau 3 com scaler",
+  "experiment_run_id": 1004,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Polynomial Regression",
+  "model_type": "polynomial_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 3,
+    "interaction_only": false,
+    "scaler_name": "Standard Scaler"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/05-linear-regression-with-lags.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão linear com lags de treino e validação",
+  "experiment_run_id": 1005,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 5
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 3
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/06-linear-regression-nan-interpolation.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão linear com tratamento de NaN por interpolação linear",
+  "experiment_run_id": 1006,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "linear interpolation",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/07-linear-regression-static-window-removal.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão linear com remoção de janelas estáticas",
+  "experiment_run_id": 1007,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": true,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/08-linear-regression-with-limits.json

@@ -0,0 +1,45 @@
+{
+  "_description": "Regressão linear com limites inferior e superior para variáveis",
+  "experiment_run_id": 1008,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {
+      "303-WIT-200(Value)": {
+        "min": 0.0,
+        "max": 1000.0
+      }
+    },
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/09-polynomial-degree2-with-scaler-and-lags.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Cenário completo: regressão polinomial grau 2 com scaler e lags",
+  "experiment_run_id": 1009,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Polynomial Regression",
+  "model_type": "polynomial_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 3
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 2
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 2,
+    "interaction_only": false,
+    "scaler_name": "Standard Scaler"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/10-linear-regression-with-ar.json

@@ -0,0 +1,42 @@
+{
+  "_description": "Linear regression placeholder for autoregressive features; include_ar is reserved for future wrapper support (see opt_params).",
+  "experiment_run_id": 1010,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {
+    "include_ar": true
+  }
+}

docs/test-scenarios/11-linear-regression-static-threshold-custom.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Regressão linear com remoção de janelas estáticas e static_threshold customizado",
+  "experiment_run_id": 1011,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": true,
+    "static_threshold": 100,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/12-angular-test-date-format.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Alternate date column (DATA) and dd/MM/yyyy HH:mm:ss format; uses MinIO object training_data_dd_mm_yyyy.csv from E2E fixtures.",
+  "experiment_run_id": 1012,
+  "variable_columns": [
+    "303-WIT-230(Value)"
+  ],
+  "target_variable": "03CV022/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data_dd_mm_yyyy.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "DATA",
+  "date_format": "dd/MM/yyyy HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-230(Value)": 3
+    },
+    "lag_val": {
+      "303-WIT-230(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": "01/05/2022",
+    "end_date": "31/07/2022",
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/13-angular-test-double-date-column.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Same alternate CSV as scenario 12 (DATA + dd/MM/yyyy); narrow date window for regression coverage. Not a multi-date-column dataset.",
+  "experiment_run_id": 1013,
+  "variable_columns": [
+    "303-WIT-230(Value)"
+  ],
+  "target_variable": "03CV022/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data_dd_mm_yyyy.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "DATA",
+  "date_format": "dd/MM/yyyy HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-230(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-230(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": "01/05/2022 00:00:00",
+    "end_date": "31/05/2022 23:59:59",
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/14-angular-test-polynomial-support-filters.json

@@ -0,0 +1,51 @@
+{
+  "_description": "Cenário angular-test-01: regressão polinomial degree 4, scaler, support filters em 303-WIT-200",
+  "experiment_run_id": 1014,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Polynomial Regression",
+  "model_type": "polynomial_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": "2025-06-02 00:00:00",
+    "end_date": "2025-06-08 23:59:59",
+    "support_filters": {
+      "303-WIT-200(Value)": {
+        "upper_line": {
+          "intercept": 40.400002,
+          "angle": 0
+        },
+        "lower_line": {
+          "intercept": 30.5,
+          "angle": 0
+        }
+      }
+    },
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 4,
+    "interaction_only": false,
+    "scaler_name": "Standard Scaler"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/15-linear-regression-custom-target-column.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Target column name is not ``target``; report/Evidently sections must use params.target_variable.",
+  "experiment_run_id": 1015,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "MY_CUSTOM_TARGET_COLUMN",
+  "bucket_name": "model-training",
+  "file_name": "training_data_custom_target.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/16-linear-regression-naive-timestamp-header.json

@@ -0,0 +1,40 @@
+{
+  "_description": "Naive Timestamp column header; snake_case date_column/date_format and training_data_timestamp_naive.csv.",
+  "experiment_run_id": 1016,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data_timestamp_naive.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "Timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/test-scenarios/17-linear-regression-blank-timestamp-row.json

@@ -0,0 +1,40 @@
+{
+  "_description": "One CSV row has an empty timestamp; pipeline should drop it and continue training.",
+  "experiment_run_id": 1017,
+  "variable_columns": [
+    "303-WIT-200(Value)"
+  ],
+  "target_variable": "03CV020/CORRENTE_N_M1_PV(Value)",
+  "bucket_name": "model-training",
+  "file_name": "training_data_blank_timestamp_row.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "date_format": "yyyy-MM-dd HH:mm:ss",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "303-WIT-200(Value)": 0
+    },
+    "lag_val": {
+      "303-WIT-200(Value)": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "None"
+  },
+  "opt_params": {}
+}

docs/train-model-workflow-io-diff-main-vs-current-branch.md

@@ -0,0 +1,417 @@
+# Train Model Workflow IO Diff (`main` vs current branch)
+
+Base comparison: `git diff main...HEAD`
+Workflow analyzed: `train_model`
+
+## 1) Executive overview
+
+This branch introduces a structural refactor of the training stack and a contract update for workflow input/output.
+
+Main impacts:
+
+- The old in-house training stack (`TrainingRepository`, `ModelRepository`, `StorageRepository`, `model_manager.sientia.models`) was replaced by:
+  - `DataManagerRepository` (data prep + metrics + report generation)
+  - `SientiaModel` wrapper from plugin store (`sientia_model`)
+  - `SientiaMLflowRepository` (MLflow integration)
+  - `MinioRepository` (storage integration)
+- Input contract moved from many fixed legacy ML params to a plugin/wrapper-oriented schema (`model_type`, `*_kwargs`, `model_metadata`, optional `val_file_name`).
+- Workflow return changed from `None` to a serializable result object (`dict[str, Any] | None`) containing training execution metadata.
+- Queue naming and worker bootstrap architecture now depend on runtime (`train_model-<runtime>-queue`).
+
+---
+
+## 2) Input contract diff (before vs now)
+
+### 2.1 Previous contract (`main`)
+
+`TrainModelParams` in `main` required a large set of explicit fields for the old preprocessing/model pipeline, focused only in linear regression model:
+
+- Core:
+  - `experiment_run_id`, `variable_columns`, `target_variable`
+  - `bucket_name`, `file_name`, `line_separator`, `decimal_separator`
+  - `train_size`, `shuffle`
+- Legacy preprocessing/model fields focused only in linear regression model (required in `from_dict`):
+  - `lag_train`, `lag_val`
+  - `rem_static_win`, `low_lim`, `upp_lim`, `window`
+  - `use_scaler`, `include_ar`, `scaler_name`
+  - `removed_intervals`, `start_date`, `end_date`, `nan_treatment`
+  - `degree`, `interaction_only`
+  - `experiment_name`, `model_name`
+  - `support_filters` (optional dict), `static_threshold` (optional int)
+
+Validation was strongly tied to this structure (lag ranges, limits consistency, polynomial/scaler constraints, etc.).
+
+### 2.2 Current contract (this branch)
+
+`TrainModelParams` now supports a plugin-driven schema and wrapper kwargs:
+
+- Kept/mandatory core fields:
+  - `experiment_run_id` (now accepts numeric string too; coerced to int)
+  - `variable_columns`, `target_variable`
+  - `bucket_name`, `file_name`, `line_separator`, `decimal_separator`
+  - `train_size`, `shuffle`
+  - `model_name`
+  - `model_type`
+  - `data_model_kwargs`, `model_kwargs`, `opt_params` (required as dict by current `from_dict`)
+- New/updated fields:
+  - `random_state` (default `42`)
+  - `val_file_name` (optional explicit validation file)
+  - `model_id` (currently optional, but needs discussion, since the model metadata in MongoDB should be created before the model training)
+- Removed from required input contract:
+  - `lag_train`, `lag_val`, `rem_static_win`, `low_lim`, `upp_lim`, `window`
+  - `use_scaler`, `include_ar`
+  - `degree`, `interaction_only`, `nan_treatment`
+  - `start_date`, `end_date`, `scaler_name`
+  - `removed_intervals`, `support_filters`, `static_threshold`
+- Parameters internally derived:
+  - `model_metadata` model type info from plugin store.
+  - `run_name` is internally derived from experiment name and datetime.
+  - `experiment_name` is internally derived from `model_name`.
+
+### 2.3 Validation behavior changes
+
+Before:
+- Validation was mostly hardcoded business checks tied to legacy linear/polynomial stack.
+
+Now:
+- Validation still checks core constraints (`train_size`, non-empty strings, etc.), but model-specific validation moved to JSON Schema driven checks, using OpenAPI/JSON Schema definitions from plugin store:
+  - `model_metadata.schemas.components.schemas.data_model`
+  - `model_metadata.schemas.components.schemas.model`
+  - `model_metadata.schemas.components.schemas.opt_params`
+- `model_metadata` is now a required semantic dependency for `validate_business_rules()`.
+- Date format validation remains, but allowed formats are defined locally in `train_model_params.py`.
+
+### 2.4 Input loading pipeline changes in workflow
+
+Before:
+- `validate_train_params` directly consumed workflow input.
+
+Now:
+1. `load_model_metadata` runs first (fetches model index/schema from plugin store and injects `model_metadata`).
+2. `validate_train_params` runs with enriched payload.
+
+This means IO preprocessing now depends on plugin-store metadata resolution before final validation.
+
+---
+
+## 3) Output contract diff (before vs now)
+
+### 3.1 Workflow return (`train_model.run`)
+
+Before (`main`):
+- Return type: `None`
+- Workflow side effects were persisted mainly via DB status updates and MLflow artifacts.
+
+Now:
+- Return type: `dict[str, Any] | None`
+- Workflow returns the training activity summary when successful.
+
+### 3.2 Activity-level training result payload
+
+Before (from `Training.train_model` in `main` path):
+- Returned minimal dict:
+  - `run_name`
+  - `run_dir`
+
+Now:
+- Returns extended dict:
+  - `run_name`
+  - `experiment_name`
+  - `run_id`
+  - `run_dir`
+
+### 3.3 Persistence map by destination (DB, MLflow, MinIO, local filesystem)
+
+This section maps where each artifact/metadata goes, in which format, and how that changed from `main`.
+
+#### 3.3.1 PostgreSQL (`experiment_run` table)
+
+## Before (`main`)
+
+- Update path: `update_experiment_run` activity with `UpdateType.MODEL_SAVED`.
+- Persisted on success:
+  - `status` transition to `TRAINING_SUCCESS`
+  - `run_name` (MLflow run identifier used by current implementation)
+- Persisted on failures:
+  - `status` transition to validation/training error statuses
+  - `error_message`
+
+## Now (current branch)
+
+- Same update path and status/error behavior.
+- Even though train activity now returns more metadata (`run_id`, `experiment_name`), current workflow update for `MODEL_SAVED` still forwards mainly `run_name`.
+- Practical effect:
+  - DB remains status-centric and run-name-centric
+  - richer identifiers exist in workflow return payload, not fully mirrored to DB columns in current flow
+
+#### 3.3.2 MLflow (tracking server/artifact store)
+
+## Before (`main`)
+
+- Persistence orchestration lived in `ModelRepository.save_model()` + `_save_run()`.
+- Typical persisted content:
+  - model params (many legacy params such as lags, limits, scaler config, removed intervals)
+  - regression metrics (`MSE`, `R2`, `MAE`)
+  - model objects:
+    - `data_model`
+    - `prediction_model`
+  - artifacts:
+    - `report.html`
+    - `train_data.csv`
+    - `test_data.csv`
+    - optional `model_equation.json`
+- Run naming:
+  - computed by querying existing runs and appending sequence (`<experiment>-<n>` style)
+
+## Now (current branch)
+
+- Persistence orchestrated in `Training._persist_training_artifacts()` and MLflow run context is opened by `SientiaMLflowRepository.start_run(...)`.
+- Persisted content now:
+  - model wrapper itself via `wrapper.store_model(name=train_params.model_name)`
+  - regression metrics also logged as MLflow params via `mlflow.log_param(...)`:
+    - `mse_val`
+    - `mae_val`
+    - `r2_val`
+  - artifacts explicitly logged with `mlflow.log_artifact(...)`:
+    - `report.html`
+    - `train_data.csv`
+    - `test_data.csv`
+  - metrics are computed before save (`mse_val`, `mae_val`, `r2_val`) and persisted in the run as params
+- Run identifiers now exposed back to workflow:
+  - `experiment_name`
+  - `run_name`
+  - `run_id`
+- Notable behavioral change:
+  - `wrapper._input_example` is cleared (`None`) before storing model.
+
+#### 3.3.3 MinIO object storage
+
+## Before (`main`)
+
+- Read path:
+  - single source object downloaded via `StorageRepository.fetch_file(bucket_name, file_name)`
+- Write path:
+  - training workflow did not write generated outputs to MinIO in this code path
+  - generated artifacts were persisted to MLflow, not uploaded back to MinIO
+- Location:
+  - source data in input bucket/key provided by workflow input (`bucket_name` + `file_name`)
+
+## Now (current branch)
+
+- Read path migrated to `MinioRepository.download_file(...)`.
+- Supports two input objects:
+  - mandatory training object: `bucket_name` + `file_name`
+  - optional validation object: same `bucket_name` + `val_file_name`
+- Write path:
+  - still no artifact upload to MinIO in this workflow path
+  - report/CSV outputs continue to flow to MLflow artifacts
+- Location details:
+  - bucket resolved from payload (`bucket_name`)
+  - object key exactly from payload (`file_name`, optional `val_file_name`)
+  - default bucket in env/config is `MINIO_DEFAULT_BUCKET`, but runtime payload can override via `bucket_name`
+
+#### 3.3.4 Local filesystem (ephemeral runtime workspace)
+
+## Before (`main`)
+
+- Temporary run dir created under reports root using run name + timestamp suffix.
+- Artifacts generated locally in that directory:
+  - `report.html`
+  - `train_data.csv`
+  - `test_data.csv`
+  - optional `model_equation.json`
+- After MLflow logging, cleanup activity removed temp directory.
+
+## Now (current branch)
+
+- Temporary run dir managed by `DataManagerRepository` under runtime reports root (`.../reports/temp/<run_name>`).
+- Same artifact family generated locally:
+  - `report.html`
+  - `train_data.csv`
+  - `test_data.csv`
+  - optional `model_equation.json` (for `linear_regression`)
+- Cleanup behavior is now tolerant:
+  - cleanup runs in guarded `finally`
+  - training success is not reverted if cleanup later fails
+
+#### 3.3.5 Quick matrix (before vs now)
+
+- **Postgres**
+  - before: status + run_name + errors
+  - now: same persisted shape; workflow return contains extra IDs
+- **MLflow**
+  - before: legacy model objects + params/metrics + report/data artifacts
+  - now: wrapper-based model persistence + `mse_val`/`mae_val`/`r2_val` as params + report/data artifacts + run_id exposed
+- **MinIO**
+  - before: reads 1 CSV input object
+  - now: reads 1 or 2 CSV input objects (train + optional validation), still no output upload
+- **Local temp**
+  - before: generated artifacts, then cleanup
+  - now: generated artifacts, then best-effort cleanup (non-blocking for success result)
+
+### 3.4 Cleanup behavior impact on output semantics
+
+Before:
+- Cleanup was called directly after training result; failures propagated straightforwardly.
+
+Now:
+- Cleanup is in a guarded `finally`.
+- If training succeeded but cleanup fails, workflow warns and does not rollback success semantics.
+- Effective output semantics: successful training result can be returned even if temp cleanup fails.
+
+---
+
+## 4) Detailed field mapping (old -> new)
+
+## Kept (or equivalent role)
+
+- `experiment_run_id` -> kept (broader accepted types: int or numeric string)
+- `variable_columns` -> kept
+- `target_variable` -> kept
+- `bucket_name` -> kept
+- `file_name` -> kept
+- `line_separator` -> kept
+- `decimal_separator` -> kept
+- `date_column` -> required (snake_case key; must exist in CSV)
+- `date_format` -> optional in payload; omitted/null/blank resolves to default `yyyy-MM-dd HH:mm:ss`
+- `train_size` -> kept
+- `shuffle` -> kept
+- `model_name` -> kept (now less coupled to legacy model enum)
+
+## Added
+
+- `model_type` (primary selector for plugin wrapper/index lookup)
+- `data_model_kwargs`
+- `model_kwargs`
+- `opt_params`
+- `val_file_name` (optional second dataset input)
+- `model_id` (optional metadata)
+- `model_metadata` (loaded/required for schema validation)
+- `random_state` (explicit split reproducibility control)
+
+## Removed from new required contract
+
+- `lag_train`, `lag_val`
+- `rem_static_win`, `static_threshold`
+- `low_lim`, `upp_lim`
+- `window`
+- `use_scaler`, `include_ar`
+- `degree`, `interaction_only`
+- `nan_treatment`
+- `start_date`, `end_date`
+- `scaler_name`
+- `removed_intervals`
+- `support_filters`
+- `experiment_name` (no longer required as top-level client input)
+
+---
+
+## 5) Internal architecture update notes
+
+### 5.1 Repository layer redesign
+
+Removed:
+- `model_manager/utils/repository/model_repository.py`
+- `model_manager/utils/repository/training_repository.py`
+- `model_manager/utils/repository/storage_repository.py`
+
+Added:
+- `model_manager/utils/repository/data_manager_repository.py`
+
+Interpretation:
+- Data preprocessing/report/metrics responsibilities were consolidated into `DataManagerRepository`.
+- Training/model persistence shifted to wrapper + plugin store + MLflow repository integrations.
+
+### 5.2 Model engine abstraction migration
+
+Before:
+- Strong coupling to local classes in `model_manager.sientia.models` and custom preprocessing/model objects in `TrainModelResult`.
+
+Now:
+- Training uses `SientiaModel` wrapper dynamically obtained by `plugin_store.get_model(model_type=...)`.
+- Contract is wrapper-driven (`train`, `transform`, `predict`, `store_model`).
+- The codebase removed `model_manager/sientia/models.py`, `model_serving.py`, and `utils.py`, indicating full migration to externalized model runtime abstraction.
+
+### 5.3 Worker/runtime architecture changes
+
+- New `prepare_worker.py` centralizes worker setup and autoscaling parameters.
+- Queue names are now runtime-derived:
+  - `train_model-<runtime>-queue`
+  - `cleanup_files-<runtime>-queue`
+- `worker.py` now installs runtime via plugin store (`plugin_store.install_runtime(runtime_name=...)`) before starting workers.
+- This introduces environment/runtime-aware deployment and model packaging behavior.
+
+### 5.4 Synchronous activity and tracking adjustments
+
+- `experiment_tracking` migrated from async postgres helper to sync postgres client path (`postgres_sync`).
+- Several activities switched to sync method signatures.
+- Error handling in workflow and DB status update paths is more defensive (secondary failures while persisting error status are logged and do not mask primary failure cause).
+
+### 5.5 `TrainModelResult` shape update
+
+Before:
+- Stored classic split artifacts (`x_train`, `x_test`, `y_train`, `y_test`) + concrete preprocessing/model objects (`process_data`, `regr`, `scaler_dict`).
+
+Now:
+- Stores `train_data`, `val_data` and prediction DataFrames, plus tracking identifiers (`experiment_name`, `run_id`).
+- Result object is less tied to internal estimator classes and more aligned with serializable workflow/model-store integration.
+
+---
+
+## 6) Net IO compatibility assessment
+
+## Input compatibility
+
+Not backward compatible with old payloads without adaptation.
+
+Key reasons:
+- Legacy required fields removed/ignored by new path.
+- New required fields introduced (`model_type`, `*_kwargs` dicts, runtime metadata flow dependency).
+- Validation pipeline now expects model metadata semantics.
+
+## Output compatibility
+
+Behavior changed:
+- Workflow now returns a result object (previously `None`).
+- Training summary includes `experiment_name` and `run_id` in addition to `run_name` and `run_dir`.
+- DB update still centered on `run_name`; callers relying only on DB may not see all new output info unless workflow return is consumed.
+
+---
+
+## 7) Practical migration guidance (client side)
+
+To call `train_model` in this branch:
+
+1. Send snake_case payload aligned to new `TrainModelParams`.
+2. Always provide:
+   - `model_name` slugified model name (ex.: `test_model_name or test-model-name`)
+   - `model_type`
+   - `data_model_kwargs` (dict)
+   - `model_kwargs` (dict)
+   - `opt_params` (dict)
+3. Keep `experiment_run_id` numeric (int or numeric string).
+4. Use runtime queue naming consistent with worker runtime:
+   - `train_model-<runtime>-queue`
+5. If you need explicit validation split file, send `val_file_name`; otherwise split uses `train_size`/`shuffle`/`random_state`.
+
+---
+
+## 8) Source references used for this document
+
+Primary diffs:
+- `model_manager/workflows/train_model.py`
+- `model_manager/utils/models/train_model_params.py`
+- `model_manager/utils/models/train_model_result.py`
+- `model_manager/activities/training.py`
+- `model_manager/activities/activities.py`
+- `model_manager/activities/experiment_tracking.py`
+- `model_manager/utils/repository/data_manager_repository.py`
+- `model_manager/utils/repository/model_repository.py` (removed)
+- `model_manager/utils/repository/training_repository.py` (removed)
+- `model_manager/utils/repository/storage_repository.py` (removed)
+- `model_manager/worker/worker.py`
+- `model_manager/worker/prepare_worker.py`
+- `README.md`
+- `input-sample.md`
+- `scripts/run_training_test.py`
+

e2e/__init__.py

@@ -0,0 +1,3 @@
+"""
+End-to-end tests for the Model Manager Temporal workflows.
+"""

e2e/conftest.py

@@ -0,0 +1,697 @@
+"""
+Pytest configuration and fixtures for E2E tests.
+
+External dependencies use testcontainers or real SDK integrations (no mocks of
+model_manager or other first-party code):
+
+- PostgreSQL, MinIO, MongoDB, Gitea: testcontainers.
+- MLflow: real client with ``file://`` tracking URI (no MLflow server process).
+- Temporal: ``WorkflowEnvironment.start_time_skipping()`` — official in-process
+  test runtime from temporalio; exercises real workflows and activity code, not
+  stubs of business logic.
+- Observability: ``Logger`` (``get_logger`` from ``model_manager.utils.logger_helper``)
+  and ``MetricsController`` from sientia_do, same stack as production.
+"""
+
+from concurrent.futures import ThreadPoolExecutor
+import base64
+import csv
+import io
+import os
+import shutil
+import tempfile
+import time
+
+import mlflow
+import pytest
+import pytest_asyncio
+import requests
+from minio import Minio
+from sqlalchemy import create_engine, text
+from testcontainers.core.container import DockerContainer  # type: ignore[import,import-untyped]
+from testcontainers.minio import MinioContainer  # type: ignore[import-untyped]
+from testcontainers.mongodb import MongoDbContainer  # type: ignore[import-untyped]
+from testcontainers.postgres import PostgresContainer  # type: ignore[import,import-untyped]
+from temporalio.testing import WorkflowEnvironment
+from temporalio.worker import Worker
+
+from model_manager.activities.activities import Activities
+from model_manager.utils.logger_helper import get_logger
+from model_manager.workflows.cleanup_files import CleanupFiles
+from model_manager.workflows.train_model import TrainModel
+from sientia_do.notifications.handlers import CoreNotificationHandler
+from sientia_do.observability.metrics_controller import MetricsController
+from sientia_model.model_repository.plugin_store import PluginStore
+
+# ---------------------------------------------------------------------------
+# CSV training data: columns must match the variable_columns and target_variable
+# used across all test scenarios.
+_TRAIN_CSV_COLUMNS = [
+    'timestamp',
+    '303-WIT-200(Value)',
+    '03CV020/CORRENTE_N_M1_PV(Value)',
+    '303-WIT-230(Value)',
+    '03CV022/CORRENTE_N_M1_PV(Value)',
+]
+_MINIO_BUCKET = 'model-training'
+_MINIO_OBJECT = 'training_data.csv'
+
+# ---------------------------------------------------------------------------
+# Helpers – CSV generation
+# ---------------------------------------------------------------------------
+
+def _build_training_csv() -> bytes:
+    """
+    Generate a 150-row CSV with all columns needed by test scenarios.
+
+    The numeric values cycle deterministically so lags and static-window
+    removal always find enough rows in both train and validation splits.
+    """
+    output = io.StringIO()
+    writer = csv.writer(output)
+    writer.writerow(_TRAIN_CSV_COLUMNS)
+    for i in range(150):
+        ts = f'2025-06-{(i // 24) + 2:02d} {i % 24:02d}:00:00'
+        wit200 = round(30.0 + (i % 20) * 0.5, 2)
+        cv020 = round(100.0 + (i % 15) * 0.3, 2)
+        wit230 = round(25.0 + (i % 18) * 0.4, 2)
+        cv022 = round(90.0 + (i % 12) * 0.25, 2)
+        writer.writerow([ts, wit200, cv020, wit230, cv022])
+    return output.getvalue().encode('utf-8')
+
+
+def _build_training_csv_dd_mm_yyyy() -> bytes:
+    """
+    Generate a 150-row CSV with dd/MM/yyyy HH:mm:ss timestamps and
+    a DATA column header, for scenarios 12/13 that use a different date format.
+    """
+    output = io.StringIO()
+    writer = csv.writer(output)
+    writer.writerow([
+        'DATA',
+        '303-WIT-230(Value)',
+        '03CV022/CORRENTE_N_M1_PV(Value)',
+    ])
+    for i in range(150):
+        day = (i % 30) + 1
+        ts = f'{day:02d}/05/2022 {i % 24:02d}:00:00'
+        wit230 = round(25.0 + (i % 18) * 0.4, 2)
+        cv022 = round(90.0 + (i % 12) * 0.25, 2)
+        writer.writerow([ts, wit230, cv022])
+    return output.getvalue().encode('utf-8')
+
+
+def _build_training_csv_custom_target_column() -> bytes:
+    """
+    Same layout as the standard CSV but the target column has a non-default name
+    (not ``target``) to exercise report and metrics paths.
+    """
+    output = io.StringIO()
+    writer = csv.writer(output)
+    writer.writerow([
+        'timestamp',
+        '303-WIT-200(Value)',
+        'MY_CUSTOM_TARGET_COLUMN',
+    ])
+    for i in range(150):
+        ts = f'2025-06-{(i // 24) + 2:02d} {i % 24:02d}:00:00'
+        wit200 = round(30.0 + (i % 20) * 0.5, 2)
+        target_val = round(100.0 + (i % 15) * 0.3, 2)
+        writer.writerow([ts, wit200, target_val])
+    return output.getvalue().encode('utf-8')
+
+
+def _build_training_csv_timestamp_header_naive() -> bytes:
+    """
+    Naive datetimes under column ``Timestamp`` (common UI export) for scenario 16.
+    """
+    output = io.StringIO()
+    writer = csv.writer(output)
+    writer.writerow([
+        'Timestamp',
+        '303-WIT-200(Value)',
+        '03CV020/CORRENTE_N_M1_PV(Value)',
+    ])
+    for i in range(150):
+        ts = f'2025-06-{(i // 24) + 2:02d} {i % 24:02d}:00:00'
+        wit200 = round(30.0 + (i % 20) * 0.5, 2)
+        cv020 = round(100.0 + (i % 15) * 0.3, 2)
+        writer.writerow([ts, wit200, cv020])
+    return output.getvalue().encode('utf-8')
+
+
+def _build_training_csv_blank_timestamp_row() -> bytes:
+    """Standard columns with one row where ``timestamp`` is empty (NaN after parse)."""
+    output = io.StringIO()
+    writer = csv.writer(output)
+    writer.writerow(_TRAIN_CSV_COLUMNS)
+    for i in range(150):
+        wit200 = round(30.0 + (i % 20) * 0.5, 2)
+        cv020 = round(100.0 + (i % 15) * 0.3, 2)
+        wit230 = round(25.0 + (i % 18) * 0.4, 2)
+        cv022 = round(90.0 + (i % 12) * 0.25, 2)
+        if i == 17:
+            writer.writerow(['', wit200, cv020, wit230, cv022])
+        else:
+            ts = f'2025-06-{(i // 24) + 2:02d} {i % 24:02d}:00:00'
+            writer.writerow([ts, wit200, cv020, wit230, cv022])
+    return output.getvalue().encode('utf-8')
+
+
+# ---------------------------------------------------------------------------
+# Helpers – Gitea seed
+# ---------------------------------------------------------------------------
+
+def _wait_for_gitea(base_url: str, timeout: int = 120) -> None:
+    """Poll Gitea until it responds to HTTP requests."""
+    deadline = time.time() + timeout
+    last_err = None
+    while time.time() < deadline:
+        try:
+            resp = requests.get(f'{base_url}/', timeout=3)
+            if resp.status_code in (200, 404, 302):
+                return
+        except Exception as e:
+            last_err = e
+        time.sleep(2)
+    raise TimeoutError(f'Gitea did not start within {timeout}s at {base_url}. Last error: {last_err}')
+
+
+def _gitea_api(method: str, url: str, auth: tuple, **kwargs) -> requests.Response:
+    resp = requests.request(method, url, auth=auth, timeout=30, **kwargs)
+    try:
+        resp.raise_for_status()
+    except requests.exceptions.HTTPError as e:
+        raise RuntimeError(f"Gitea API error {resp.status_code}: {resp.text}") from e
+    return resp
+
+
+def _seed_gitea(base_url: str, admin_user: str, admin_pass: str) -> None:
+    """
+    Create a fictitious model-store repository with dummy models.
+    """
+    auth = (admin_user, admin_pass)
+    api = f'{base_url}/api/v1'
+
+    # Create repository
+    _gitea_api(
+        'POST', f'{api}/user/repos', auth,
+        json={'name': 'model-store', 'private': False, 'auto_init': False},
+    )
+
+    # Root index.yaml
+    root_index = """
+store_name: "E2E Test Store"
+version: 1
+models:
+  - name: "linear_regression"
+    version: 1
+    runtime: "basic"
+  - name: "polynomial_regression"
+    version: 1
+    runtime: "basic"
+runtimes:
+  basic:
+    version: "1.0.0"
+    libraries:
+      - name: "pandas"
+      - name: "numpy"
+"""
+
+    # Model index.yaml (shared for all dummies)
+    model_index = """
+name: "{model_name}"
+version: 1
+runtime: "basic"
+path: "wrapper.py"
+class: "DummyWrapper"
+model:
+  class: "DummyModel"
+  path: "model_logic.py"
+  external: false
+data_model:
+  class: "DummyTransformer"
+  path: "model_logic.py"
+  external: false
+"""
+
+    # schemas.yaml
+    schemas_yaml = """
+model:
+  type: object
+  properties: {}
+data_model:
+  type: object
+  properties: {}
+opt_params:
+  type: object
+  properties: {}
+"""
+
+    # wrapper.py
+    wrapper_py = """
+from sientia_model.wrappers.sientia_model import SientiaModel
+import pandas as pd
+import numpy as np
+from typing import Any
+
+class DummyWrapper(SientiaModel):
+    def _predict(self, data: pd.DataFrame) -> tuple[pd.DataFrame, dict[str, Any]]:
+        self._log("info", f"Predicting dummy model for {self.model_type}")
+        # Return a simple prediction (mean or 0.5) to allow metrics computation
+        preds = pd.DataFrame({self.target: [0.5] * len(data)}, index=data.index)
+        return preds, {}
+        
+    def _transform(self, data: pd.DataFrame) -> tuple[pd.DataFrame, dict[str, Any]]:
+        return data, {}
+        
+    def _train_transformer(self, train_data: pd.DataFrame, val_data: pd.DataFrame) -> None:
+        pass
+        
+    def _train_model(self, x: pd.DataFrame, y: pd.DataFrame, x_val: pd.DataFrame | None = None, y_val: pd.DataFrame | None = None) -> None:
+        self.target = y.columns[0]
+        
+    def _retrain_transformer(self, data: pd.DataFrame) -> None:
+        pass
+        
+    def _retrain_model(self, x: pd.DataFrame, y: pd.DataFrame | None) -> None:
+        pass
+"""
+
+    # model_logic.py
+    model_logic_py = """
+class DummyModel:
+    def __init__(self, **kwargs):
+        pass
+
+class DummyTransformer:
+    def __init__(self, **kwargs):
+        pass
+"""
+
+    # requirements.txt required by current model packaging path in training activity.
+    requirements_txt = """
+pandas
+numpy
+"""
+
+    def push_file(path: str, content: str):
+        encoded = base64.b64encode(content.encode()).decode()
+        _gitea_api(
+            'POST',
+            f'{api}/repos/{admin_user}/model-store/contents/{path}',
+            auth,
+            json={'message': f'seed: {path}', 'content': encoded},
+        )
+
+    # Push root index
+    push_file('index.yaml', root_index)
+
+    # Push files for both models used in tests
+    for model_name in ['linear_regression', 'polynomial_regression']:
+        prefix = f'models/{model_name}'
+        push_file(f'{prefix}/index.yaml', model_index.format(model_name=model_name))
+        push_file(f'{prefix}/schemas.yaml', schemas_yaml)
+        push_file(f'{prefix}/wrapper.py', wrapper_py)
+        push_file(f'{prefix}/model_logic.py', model_logic_py)
+        push_file(f'{prefix}/requirements.txt', requirements_txt.strip() + '\n')
+        push_file(f'{prefix}/__init__.py', "")
+
+    # Push runtime
+    push_file('runtime/basic.yaml', 'name: basic\nversion: "1.0.0"\nlibraries: []')
+
+
+# ---------------------------------------------------------------------------
+# Session-scoped containers
+# ---------------------------------------------------------------------------
+
+@pytest.fixture(scope='session')
+def postgres_container():
+    """PostgreSQL 15 container for experiment_run table."""
+    container = PostgresContainer('postgres:15')
+    container.start()
+    yield container
+    container.stop()
+
+
+@pytest.fixture(scope='session')
+def minio_container():
+    """MinIO container for training CSV storage."""
+    container = MinioContainer()
+    container.start()
+    yield container
+    container.stop()
+
+
+@pytest.fixture(scope='session')
+def mongodb_container():
+    """MongoDB container for CoreNotificationHandler."""
+    container = MongoDbContainer('mongo:7')
+    container.start()
+    yield container
+    container.stop()
+
+
+@pytest.fixture(scope='session')
+def gitea_container():
+    """
+    Gitea container with a ``model-store`` repo seeded via REST API
+    (``index.yaml``, dummy model files, ``_seed_gitea``).
+
+    The container starts with INSTALL_LOCK so no setup wizard is needed.
+    An admin user is created via Gitea's CLI before the HTTP API is used.
+    """
+    admin_user = 'gitea_admin'
+    admin_pass = 'gitea_admin_pass'  # noqa: S105
+
+    container = (
+        DockerContainer('gitea/gitea:latest')
+        .with_env('GITEA__security__INSTALL_LOCK', 'true')
+        .with_env('GITEA__server__HTTP_PORT', '3000')
+        .with_env('GITEA__log__LEVEL', 'Warn')
+        .with_exposed_ports(3000)
+    )
+    container.start()
+
+    port = container.get_exposed_port(3000)
+    base_url = f'http://localhost:{port}'
+
+    _wait_for_gitea(base_url)
+    time.sleep(5)  # Wait a bit for DB to fully initialize after HTTP is up
+
+    # Create admin user via Gitea CLI inside the container
+    # Must run after Gitea is fully initialized
+    gitea_cmd = (
+        f'gitea admin user create '
+        f'--username {admin_user} '
+        f'--password {admin_pass} '
+        f'--email admin@test.local '
+        f'--admin '
+        f'--must-change-password=false'
+    )
+    exec_result = container.exec(f"su git -c '{gitea_cmd}'")
+    if exec_result.exit_code != 0:
+        raise RuntimeError(f"Failed to create Gitea admin user: {exec_result.output.decode('utf-8')}")
+
+    _seed_gitea(base_url, admin_user, admin_pass)
+
+    yield {
+        'container': container,
+        'base_url': base_url,
+        'admin_user': admin_user,
+        'admin_pass': admin_pass,
+    }
+
+    container.stop()
+
+
+@pytest.fixture(scope='session')
+def mlflow_tracking_dir():
+    """Local MLflow filesystem tracking directory (no network needed)."""
+    tmpdir = tempfile.mkdtemp(prefix='mlflow-e2e-')
+    mlflow.set_tracking_uri(f'file://{tmpdir}')
+    yield tmpdir
+    shutil.rmtree(tmpdir, ignore_errors=True)
+
+
+@pytest.fixture(scope='session', autouse=True)
+def e2e_runtime_reports_dir():
+    """
+    Route runtime report artifacts to a writable temp directory during E2E.
+
+    Production defaults point to /var/lib/model-manager; in local CI/dev runs this
+    path may be unavailable. This fixture keeps the same code paths while avoiding
+    host permission issues.
+    """
+    import model_manager.runtime_paths as runtime_paths
+    import model_manager.utils.repository.data_manager_repository as data_repo_module
+
+    base_dir = tempfile.mkdtemp(prefix='model-manager-e2e-runtime-')
+    reports_root = f'{base_dir}/reports'
+    reports_temp_dir = f'{reports_root}/temp'
+    project_base_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'model_manager'))
+
+    old_runtime_reports_root = runtime_paths.REPORTS_ROOT
+    old_runtime_reports_temp = runtime_paths.REPORTS_TEMP_DIR
+    old_runtime_project_base = runtime_paths.PROJECT_BASE_PATH
+    old_repo_reports_root = data_repo_module.REPORTS_ROOT
+    old_repo_project_base = data_repo_module.PROJECT_BASE_PATH
+
+    runtime_paths.REPORTS_ROOT = reports_root
+    runtime_paths.REPORTS_TEMP_DIR = reports_temp_dir
+    runtime_paths.PROJECT_BASE_PATH = project_base_path
+    data_repo_module.REPORTS_ROOT = reports_root
+    data_repo_module.PROJECT_BASE_PATH = project_base_path
+
+    try:
+        yield reports_root
+    finally:
+        runtime_paths.REPORTS_ROOT = old_runtime_reports_root
+        runtime_paths.REPORTS_TEMP_DIR = old_runtime_reports_temp
+        runtime_paths.PROJECT_BASE_PATH = old_runtime_project_base
+        data_repo_module.REPORTS_ROOT = old_repo_reports_root
+        data_repo_module.PROJECT_BASE_PATH = old_repo_project_base
+        shutil.rmtree(base_dir, ignore_errors=True)
+
+
+# ---------------------------------------------------------------------------
+# Session-scoped: seed MinIO with training CSV
+# ---------------------------------------------------------------------------
+
+@pytest.fixture(scope='session', autouse=True)
+def upload_training_csv(minio_container, mlflow_tracking_dir):  # noqa: ARG001
+    """
+    Upload training CSV files to the MinIO container before any test runs.
+    Depends on mlflow_tracking_dir to ensure the MLflow URI is set at session start.
+    """
+    port = minio_container.get_exposed_port(9000)
+    client = Minio(
+        f'localhost:{port}',
+        access_key='minioadmin',
+        secret_key='minioadmin',
+        secure=False,
+    )
+
+    if not client.bucket_exists(_MINIO_BUCKET):
+        client.make_bucket(_MINIO_BUCKET)
+
+    # Standard training CSV
+    csv_bytes = _build_training_csv()
+    client.put_object(
+        _MINIO_BUCKET,
+        _MINIO_OBJECT,
+        io.BytesIO(csv_bytes),
+        length=len(csv_bytes),
+        content_type='text/csv',
+    )
+
+    # dd/MM/yyyy format CSV for scenarios 12/13
+    alt_csv_bytes = _build_training_csv_dd_mm_yyyy()
+    client.put_object(
+        _MINIO_BUCKET,
+        'training_data_dd_mm_yyyy.csv',
+        io.BytesIO(alt_csv_bytes),
+        length=len(alt_csv_bytes),
+        content_type='text/csv',
+    )
+
+    custom_target = _build_training_csv_custom_target_column()
+    client.put_object(
+        _MINIO_BUCKET,
+        'training_data_custom_target.csv',
+        io.BytesIO(custom_target),
+        length=len(custom_target),
+        content_type='text/csv',
+    )
+
+    ts_header = _build_training_csv_timestamp_header_naive()
+    client.put_object(
+        _MINIO_BUCKET,
+        'training_data_timestamp_naive.csv',
+        io.BytesIO(ts_header),
+        length=len(ts_header),
+        content_type='text/csv',
+    )
+
+    blank_ts = _build_training_csv_blank_timestamp_row()
+    client.put_object(
+        _MINIO_BUCKET,
+        'training_data_blank_timestamp_row.csv',
+        io.BytesIO(blank_ts),
+        length=len(blank_ts),
+        content_type='text/csv',
+    )
+
+
+# ---------------------------------------------------------------------------
+# Function-scoped: database engine + schema setup
+# ---------------------------------------------------------------------------
+
+@pytest.fixture
+def postgres_engine(postgres_container):
+    """SQLAlchemy engine connected to the test PostgreSQL container."""
+    engine = create_engine(postgres_container.get_connection_url())
+    yield engine
+    engine.dispose()
+
+
+@pytest.fixture(autouse=True)
+def setup_experiment_run_table(postgres_engine):
+    """
+    Create the experiment_run table before each test and drop it afterwards
+    to guarantee full isolation between tests.
+    """
+    with postgres_engine.begin() as conn:
+        conn.execute(text("""
+            CREATE TABLE IF NOT EXISTS public.experiment_run (
+                id              INT PRIMARY KEY,
+                experiment_name TEXT NOT NULL,
+                run_name        TEXT,
+                username        TEXT,
+                status          TEXT NOT NULL DEFAULT 'ORCHESTRATOR_WAITING_PROC',
+                error_message   TEXT,
+                created_at      TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+                updated_at      TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+                bucket_name     TEXT,
+                file_name       TEXT
+            )
+        """))
+    yield
+    with postgres_engine.begin() as conn:
+        conn.execute(text('DROP TABLE IF EXISTS public.experiment_run'))
+
+
+# ---------------------------------------------------------------------------
+# Observability (real sientia_do implementations)
+# ---------------------------------------------------------------------------
+
+@pytest.fixture(scope='session')
+def e2e_logger():
+    """Shared production-style Logger for the whole E2E session."""
+    return get_logger('model-manager-e2e')
+
+
+@pytest.fixture
+def metrics_controller(e2e_logger):
+    """MetricsController bound to the E2E logger (fresh instance per test)."""
+    return MetricsController(logger=e2e_logger)
+
+
+# ---------------------------------------------------------------------------
+# Application fixtures
+# ---------------------------------------------------------------------------
+
+@pytest.fixture
+def notification_handler(mongodb_container, e2e_logger):
+    """
+    Real CoreNotificationHandler connected to the MongoDB testcontainer.
+    """
+    connection_url = mongodb_container.get_connection_url()
+    handler = CoreNotificationHandler(
+        connection_string=connection_url,
+        database='test_notifications',
+        logger=e2e_logger,
+        project_name='model-manager-e2e',
+    )
+    yield handler
+    handler.shutdown()
+
+
+@pytest.fixture
+def plugin_store(gitea_container, e2e_logger, metrics_controller, notification_handler):
+    """
+    Real PluginStore pointed at the Gitea testcontainer.
+    cache_ttl_seconds=0 forces a fresh download every test.
+    """
+    store = PluginStore(
+        base_url=gitea_container['base_url'],
+        owner=gitea_container['admin_user'],
+        repo='model-store',
+        username=gitea_container['admin_user'],
+        password=gitea_container['admin_pass'],
+        cache_ttl_seconds=0,
+        logger=e2e_logger,
+        notification_handler=notification_handler,
+        metrics_controller=metrics_controller,
+    )
+    yield store
+
+
+@pytest.fixture
+def test_activities(
+    postgres_container,
+    minio_container,
+    mlflow_tracking_dir,  # noqa: ARG001 – ensures MLflow URI is set
+    plugin_store,
+    e2e_logger,
+    notification_handler,
+    metrics_controller,
+):
+    """
+    Real Activities instance wired to all testcontainers.
+    """
+    pg_port = postgres_container.get_exposed_port(5432)
+    minio_port = minio_container.get_exposed_port(9000)
+
+    activities = Activities(
+        postgres_config={
+            'host': 'localhost',
+            'port': int(pg_port),
+            'user': 'test',
+            'password': 'test',
+            'dbname': 'test',
+            'min_connections': 1,
+            'max_connections': 5,
+        },
+        mlflow_config={
+            'url': mlflow.get_tracking_uri(),
+            'username': None,
+            'password': None,
+        },
+        minio_config={
+            'endpoint_url': f'http://localhost:{minio_port}',
+            'access_key': 'minioadmin',
+            'secret_key': 'minioadmin',
+            'use_ssl': False,
+            'default_bucket': _MINIO_BUCKET,
+        },
+        plugin_store=plugin_store,
+        logger=e2e_logger,
+        notification_handler=notification_handler,
+        metrics_controller=metrics_controller,
+    )
+    yield activities
+    activities.shutdown()
+
+
+def _activity_list(activities: Activities) -> list:
+    return [
+        activities.update_experiment_run,
+        activities.load_model_metadata,
+        activities.validate_train_params,
+        activities.train_model,
+        activities.cleanup_resources,
+        activities.cleanup_temp_directories,
+    ]
+
+
+@pytest_asyncio.fixture(scope='function')
+async def temporal_test_env():
+    """Temporal SDK test environment (time-skipping); runs real workflow/activity code."""
+    env = await WorkflowEnvironment.start_time_skipping()
+    async with env:
+        yield env
+
+
+@pytest_asyncio.fixture(scope='function')
+async def temporal_worker(temporal_test_env, test_activities):
+    """Temporal worker registered with all workflows and activities."""
+    with ThreadPoolExecutor() as executor:
+        async with Worker(
+            temporal_test_env.client,
+            task_queue='test-queue',
+            workflows=[TrainModel, CleanupFiles],
+            activities=_activity_list(test_activities),
+            activity_executor=executor,
+        ) as worker:
+            yield worker

e2e/helpers.py

@@ -0,0 +1,195 @@
+"""
+Shared helpers for E2E tests (Temporal workflows + PostgreSQL).
+"""
+
+import asyncio
+import json
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+import pytest
+from sqlalchemy import text
+from sqlalchemy.engine import Engine
+
+
+async def start_and_await_workflow(
+    client,
+    workflow_run,
+    input_data: dict,
+    workflow_id: str,
+    timeout: float = 600.0,
+):
+    """
+    Start a Temporal workflow and wait for its result.
+
+    Args:
+        client: Temporal client from WorkflowEnvironment.
+        workflow_run: Workflow run method (e.g. TrainModel.run).
+        input_data: Workflow input payload.
+        workflow_id: Unique workflow id.
+        timeout: Max seconds to wait for completion (default allows cold testcontainer startup).
+
+    Returns:
+        Workflow result value.
+    """
+    handle = await client.start_workflow(
+        workflow_run,
+        input_data,
+        id=workflow_id,
+        task_queue='test-queue',
+    )
+    return await asyncio.wait_for(handle.result(), timeout=timeout)
+
+
+def make_workflow_id(prefix: str) -> str:
+    """Build a unique workflow id using a prefix and current timestamp."""
+    return f'{prefix}-{datetime.now().timestamp()}'
+
+
+def insert_experiment_run(
+    engine: Engine,
+    experiment_run_id: int,
+    experiment_name: str = 'test_experiment',
+    status: str = 'ORCHESTRATOR_WAITING_PROC',
+    bucket_name: str = 'model-training',
+    file_name: str = 'training_data.csv',
+) -> None:
+    """
+    Insert a minimal experiment_run row to satisfy foreign-key-style lookups.
+
+    Args:
+        engine: SQLAlchemy engine connected to the test database.
+        experiment_run_id: Primary key for the row.
+        experiment_name: Human-readable experiment name.
+        status: Initial status string.
+        bucket_name: MinIO bucket name.
+        file_name: Training file name inside the bucket.
+    """
+    with engine.begin() as conn:
+        conn.execute(
+            text("""
+                INSERT INTO public.experiment_run
+                    (id, experiment_name, status, bucket_name, file_name)
+                VALUES
+                    (:id, :experiment_name, :status, :bucket_name, :file_name)
+                ON CONFLICT (id) DO NOTHING
+            """),
+            {
+                'id': experiment_run_id,
+                'experiment_name': experiment_name,
+                'status': status,
+                'bucket_name': bucket_name,
+                'file_name': file_name,
+            },
+        )
+
+
+def assert_experiment_status(
+    engine: Engine,
+    experiment_run_id: int,
+    expected_status: str,
+) -> None:
+    """
+    Assert the final status of an experiment_run row.
+
+    Args:
+        engine: SQLAlchemy engine.
+        experiment_run_id: Row primary key.
+        expected_status: Expected status string.
+    """
+    with engine.connect() as conn:
+        row = conn.execute(
+            text('SELECT status FROM public.experiment_run WHERE id = :id'),
+            {'id': experiment_run_id},
+        ).fetchone()
+
+    assert row is not None, (
+        f'No experiment_run row found for id={experiment_run_id}'
+    )
+    assert row[0] == expected_status, (
+        f'Expected status={expected_status!r}, got {row[0]!r} '
+        f'for experiment_run id={experiment_run_id}'
+    )
+
+
+def assert_experiment_run_name_set(
+    engine: Engine,
+    experiment_run_id: int,
+) -> None:
+    """Assert that run_name is not null/empty after a successful training."""
+    with engine.connect() as conn:
+        row = conn.execute(
+            text('SELECT run_name FROM public.experiment_run WHERE id = :id'),
+            {'id': experiment_run_id},
+        ).fetchone()
+
+    assert row is not None, (
+        f'No experiment_run row found for id={experiment_run_id}'
+    )
+    assert row[0] is not None and row[0].strip() != '', (
+        f'Expected run_name to be set for experiment_run id={experiment_run_id}, got {row[0]!r}'
+    )
+
+
+def assert_experiment_error(
+    engine: Engine,
+    experiment_run_id: int,
+    expected_status: str,
+    error_substr: str,
+) -> None:
+    """
+    Assert status and that error_message contains a given substring.
+
+    Args:
+        engine: SQLAlchemy engine.
+        experiment_run_id: Row primary key.
+        expected_status: Expected status string.
+        error_substr: Substring that must appear in error_message.
+    """
+    with engine.connect() as conn:
+        row = conn.execute(
+            text(
+                'SELECT status, error_message FROM public.experiment_run WHERE id = :id'
+            ),
+            {'id': experiment_run_id},
+        ).fetchone()
+
+    assert row is not None, (
+        f'No experiment_run row found for id={experiment_run_id}'
+    )
+    assert row[0] == expected_status, (
+        f'Expected status={expected_status!r}, got {row[0]!r}'
+    )
+    assert row[1] is not None and error_substr.lower() in row[1].lower(), (
+        f'Expected error_message to contain {error_substr!r}, got {row[1]!r}'
+    )
+
+
+def assert_no_experiment_row(engine: Engine, experiment_run_id: int) -> None:
+    """Assert that no experiment_run row exists for the given id."""
+    with engine.connect() as conn:
+        count = conn.execute(
+            text('SELECT COUNT(*) FROM public.experiment_run WHERE id = :id'),
+            {'id': experiment_run_id},
+        ).scalar()
+    assert count == 0, (
+        f'Expected no experiment_run row for id={experiment_run_id}, found {count}'
+    )
+
+
+def load_scenario(scenario_filename: str) -> dict[str, Any]:
+    """
+    Load a test scenario JSON file from docs/test-scenarios/.
+
+    Args:
+        scenario_filename: Filename without path (e.g. '01-linear-regression-basic.json').
+
+    Returns:
+        dict: Parsed scenario payload.
+    """
+    scenario_path = (
+        Path(__file__).parent.parent / 'docs' / 'test-scenarios' / scenario_filename
+    )
+    with open(scenario_path) as f:
+        return json.load(f)

e2e/test_cleanup_files_workflow.py

@@ -0,0 +1,108 @@
+"""
+End-to-end tests for CleanupFiles workflow.
+
+Covers scenarios 3.x: cleanup of temporary local directories.
+"""
+
+import pytest
+from temporalio.testing import WorkflowEnvironment
+from temporalio.worker import Worker
+
+from e2e.helpers import make_workflow_id, start_and_await_workflow
+from model_manager.workflows.cleanup_files import CleanupFiles
+
+# Matches Cleanup.dir_timestamp_pattern: name_YYYYMMDD_HHMMSS_microseconds
+_STALE_DIR_OLD = 'stale_run_20200102_030405_000001'
+_STALE_DIR_OLDER = 'stale_run_20191231_235959_999999'
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_3_1_1_cleanup_with_no_temp_dirs(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    tmp_path,
+):
+    """Scenario 3.1.1 – Cleanup when the temp directory is empty.
+
+    The cleanup_temp_directories activity should complete without error
+    and the workflow should finish successfully.
+    """
+    # Use an empty temp directory as the reports path
+    empty_dir = tmp_path / 'reports_temp'
+    empty_dir.mkdir()
+
+    result = await start_and_await_workflow(
+        temporal_test_env.client,
+        CleanupFiles.run,
+        {'temp_path': str(empty_dir)},
+        make_workflow_id('test-s3-1-1'),
+    )
+
+    # Workflow returns None on success
+    assert result is None
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_3_1_2_cleanup_removes_old_temp_dirs(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    tmp_path,
+):
+    """Scenario 3.1.2 – Cleanup removes stale subdirectories from the temp dir.
+
+    Creates two subdirectories with timestamp suffixes inside the reports
+    temp directory and verifies the activity removes them.
+    """
+    reports_dir = tmp_path / 'reports_temp'
+    reports_dir.mkdir()
+
+    # Create two stale run directories (names must match cleanup activity regex)
+    stale1 = reports_dir / _STALE_DIR_OLD
+    stale2 = reports_dir / _STALE_DIR_OLDER
+    stale1.mkdir()
+    stale2.mkdir()
+    (stale1 / 'model.pkl').write_bytes(b'fake-model-data')
+    (stale2 / 'report.json').write_bytes(b'{"status": "old"}')
+
+    result = await start_and_await_workflow(
+        temporal_test_env.client,
+        CleanupFiles.run,
+        {'temp_path': str(reports_dir)},
+        make_workflow_id('test-s3-1-2'),
+    )
+
+    assert result is None
+
+    # The activity should have cleaned up the stale directories
+    remaining = list(reports_dir.iterdir())
+    assert len(remaining) == 0, (
+        f'Expected all stale dirs to be removed, but found: {remaining}'
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_3_1_3_cleanup_nonexistent_temp_path(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    tmp_path,
+):
+    """Scenario 3.1.3 – Cleanup with a temp_path that does not exist.
+
+    The activity must handle a missing directory gracefully without
+    raising an unhandled exception, since the directory may have already
+    been cleaned by a previous run.
+    """
+    nonexistent = str(tmp_path / 'does_not_exist' / 'reports')
+
+    # Should not raise — the activity is expected to handle a missing path
+    result = await start_and_await_workflow(
+        temporal_test_env.client,
+        CleanupFiles.run,
+        {'temp_path': nonexistent},
+        make_workflow_id('test-s3-1-3'),
+    )
+
+    assert result is None

e2e/test_train_model_validation.py

@@ -0,0 +1,298 @@
+"""
+End-to-end tests for TrainModel parameter validation paths.
+
+Covers scenarios 2.1.x: workflows that must terminate with
+ORCHESTRATOR_VALIDATION_ERROR due to invalid parameter values.
+"""
+
+import pytest
+from temporalio.client import WorkflowFailureError
+from temporalio.testing import WorkflowEnvironment
+from temporalio.worker import Worker
+
+from e2e.helpers import (
+    assert_experiment_error,
+    insert_experiment_run,
+    load_scenario,
+    make_workflow_id,
+    start_and_await_workflow,
+)
+from model_manager.workflows.train_model import TrainModel
+
+# Base experiment_run ids for validation test scenarios (offset to avoid collision)
+_VALIDATION_ID_BASE = 3000
+
+
+def _exception_chain_text(exc: BaseException) -> str:
+    """Concatenate messages from an exception __cause__/__context__ chain."""
+    parts: list[str] = []
+    cur: BaseException | None = exc
+    seen: set[int] = set()
+    while cur is not None and id(cur) not in seen:
+        seen.add(id(cur))
+        text = str(cur).strip()
+        if text:
+            parts.append(text)
+        cur = cur.__cause__ or getattr(cur, '__context__', None)
+    return ' | '.join(parts).lower()
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_2_1_1_train_size_out_of_range(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 2.1.1 – train_size=5 violates the 10–100 business rule.
+
+    Expected: workflow updates status → ORCHESTRATOR_VALIDATION_ERROR
+    and error_message references 'train_size'.
+    """
+    experiment_run_id = _VALIDATION_ID_BASE + 1
+    scenario = load_scenario('01-linear-regression-basic.json')
+    scenario = {**scenario, 'experiment_run_id': experiment_run_id, 'train_size': 5}
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    with pytest.raises(Exception):
+        await start_and_await_workflow(
+            temporal_test_env.client,
+            TrainModel.run,
+            scenario,
+            make_workflow_id('test-s2-1-1'),
+        )
+
+    assert_experiment_error(
+        postgres_engine,
+        experiment_run_id,
+        expected_status='ORCHESTRATOR_VALIDATION_ERROR',
+        error_substr='train_size',
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_2_1_2_empty_variable_columns(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 2.1.2 – variable_columns=[] → ORCHESTRATOR_VALIDATION_ERROR."""
+    experiment_run_id = _VALIDATION_ID_BASE + 2
+    scenario = load_scenario('01-linear-regression-basic.json')
+    scenario = {**scenario, 'experiment_run_id': experiment_run_id, 'variable_columns': []}
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    with pytest.raises(Exception):
+        await start_and_await_workflow(
+            temporal_test_env.client,
+            TrainModel.run,
+            scenario,
+            make_workflow_id('test-s2-1-2'),
+        )
+
+    assert_experiment_error(
+        postgres_engine,
+        experiment_run_id,
+        expected_status='ORCHESTRATOR_VALIDATION_ERROR',
+        error_substr='variable_columns',
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_2_1_3_invalid_date_format(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 2.1.3 – date_format='INVALID' is not in the allowed list."""
+    experiment_run_id = _VALIDATION_ID_BASE + 3
+    scenario = load_scenario('01-linear-regression-basic.json')
+    scenario = {**scenario, 'experiment_run_id': experiment_run_id, 'date_format': 'INVALID'}
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    with pytest.raises(Exception):
+        await start_and_await_workflow(
+            temporal_test_env.client,
+            TrainModel.run,
+            scenario,
+            make_workflow_id('test-s2-1-3'),
+        )
+
+    assert_experiment_error(
+        postgres_engine,
+        experiment_run_id,
+        expected_status='ORCHESTRATOR_VALIDATION_ERROR',
+        error_substr='date_format',
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_2_1_4_whitespace_only_model_name(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 2.1.4 – model_name='   ' (whitespace) → ORCHESTRATOR_VALIDATION_ERROR."""
+    experiment_run_id = _VALIDATION_ID_BASE + 4
+    scenario = load_scenario('01-linear-regression-basic.json')
+    scenario = {**scenario, 'experiment_run_id': experiment_run_id, 'model_name': '   '}
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    with pytest.raises(Exception):
+        await start_and_await_workflow(
+            temporal_test_env.client,
+            TrainModel.run,
+            scenario,
+            make_workflow_id('test-s2-1-4'),
+        )
+
+    assert_experiment_error(
+        postgres_engine,
+        experiment_run_id,
+        expected_status='ORCHESTRATOR_VALIDATION_ERROR',
+        error_substr='model_name',
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_2_1_5_unknown_model_type(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 2.1.5 – model_type='totally_unknown' → ORCHESTRATOR_VALIDATION_ERROR.
+
+    The PluginStore will not find this model in the Gitea repo, causing
+    load_model_metadata to fail before validate_train_params is even called.
+    """
+    experiment_run_id = _VALIDATION_ID_BASE + 5
+    scenario = load_scenario('01-linear-regression-basic.json')
+    scenario = {
+        **scenario,
+        'experiment_run_id': experiment_run_id,
+        'model_type': 'totally_unknown_model',
+    }
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    with pytest.raises(Exception):
+        await start_and_await_workflow(
+            temporal_test_env.client,
+            TrainModel.run,
+            scenario,
+            make_workflow_id('test-s2-1-5'),
+        )
+
+    assert_experiment_error(
+        postgres_engine,
+        experiment_run_id,
+        expected_status='ORCHESTRATOR_VALIDATION_ERROR',
+        error_substr='totally_unknown_model',
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_2_1_6_missing_target_variable(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 2.1.6 – target_variable='' (empty string) → ORCHESTRATOR_VALIDATION_ERROR."""
+    experiment_run_id = _VALIDATION_ID_BASE + 6
+    scenario = load_scenario('01-linear-regression-basic.json')
+    scenario = {**scenario, 'experiment_run_id': experiment_run_id, 'target_variable': ''}
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    with pytest.raises(Exception):
+        await start_and_await_workflow(
+            temporal_test_env.client,
+            TrainModel.run,
+            scenario,
+            make_workflow_id('test-s2-1-6'),
+        )
+
+    assert_experiment_error(
+        postgres_engine,
+        experiment_run_id,
+        expected_status='ORCHESTRATOR_VALIDATION_ERROR',
+        error_substr='target_variable',
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_2_1_7_missing_experiment_run_id(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+):
+    """Scenario 2.1.7 – experiment_run_id missing → workflow raises ValueError immediately.
+
+    No DB row is inserted because experiment_run_id is mandatory to even
+    know which row to update. The workflow should raise before any DB call.
+    """
+    scenario = load_scenario('01-linear-regression-basic.json')
+    scenario = {k: v for k, v in scenario.items() if k != 'experiment_run_id'}
+
+    with pytest.raises(WorkflowFailureError) as excinfo:
+        await start_and_await_workflow(
+            temporal_test_env.client,
+            TrainModel.run,
+            scenario,
+            make_workflow_id('test-s2-1-7'),
+        )
+    combined = _exception_chain_text(excinfo.value)
+    assert 'experiment_run_id' in combined
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_2_1_8_missing_date_column(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+):
+    """Scenario 2.1.8 – date_column missing in payload raises before workflow business validation."""
+    scenario = load_scenario('01-linear-regression-basic.json')
+    scenario = {k: v for k, v in scenario.items() if k != 'date_column'}
+
+    with pytest.raises(WorkflowFailureError) as excinfo:
+        await start_and_await_workflow(
+            temporal_test_env.client,
+            TrainModel.run,
+            scenario,
+            make_workflow_id('test-s2-1-8'),
+        )
+    combined = _exception_chain_text(excinfo.value)
+    assert 'date_column' in combined
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_2_1_9_whitespace_date_column(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 2.1.9 – date_column='   ' must produce ORCHESTRATOR_VALIDATION_ERROR."""
+    experiment_run_id = _VALIDATION_ID_BASE + 9
+    scenario = load_scenario('01-linear-regression-basic.json')
+    scenario = {**scenario, 'experiment_run_id': experiment_run_id, 'date_column': '   '}
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    with pytest.raises(Exception):
+        await start_and_await_workflow(
+            temporal_test_env.client,
+            TrainModel.run,
+            scenario,
+            make_workflow_id('test-s2-1-9'),
+        )
+
+    assert_experiment_error(
+        postgres_engine,
+        experiment_run_id,
+        expected_status='ORCHESTRATOR_VALIDATION_ERROR',
+        error_substr='date_column',
+    )

e2e/test_train_model_workflow.py

@@ -0,0 +1,493 @@
+"""
+End-to-end tests for TrainModel workflow – main workflow scenarios.
+
+Covers:
+  1.1.x – Happy-path training (various scenarios from docs/test-scenarios/)
+  1.2.x – Error paths (MinIO failure, missing DB row)
+"""
+
+import pytest
+from temporalio.testing import WorkflowEnvironment
+from temporalio.worker import Worker
+
+from e2e.helpers import (
+    assert_experiment_error,
+    assert_experiment_run_name_set,
+    assert_experiment_status,
+    assert_no_experiment_row,
+    insert_experiment_run,
+    load_scenario,
+    make_workflow_id,
+    start_and_await_workflow,
+)
+from model_manager.workflows.train_model import TrainModel
+
+
+# ---------------------------------------------------------------------------
+# 1.1 – Happy paths
+# ---------------------------------------------------------------------------
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_1_linear_regression_basic(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.1 – Linear Regression Basic (cenário 01).
+
+    Validates the complete training pipeline end-to-end:
+    load_model_metadata → validate_train_params → train_model →
+    update_experiment_run (TRAINING_SUCCESS).
+    """
+    scenario = load_scenario('01-linear-regression-basic.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-1'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+    assert_experiment_run_name_set(postgres_engine, experiment_run_id)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_2_linear_regression_with_scaler(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.2 – Linear regression with Standard Scaler (cenário 02)."""
+    scenario = load_scenario('02-linear-regression-with-scaler.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-2'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+    assert_experiment_run_name_set(postgres_engine, experiment_run_id)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_3_polynomial_regression_degree2_with_scaler(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.3 – Polynomial Regression Degree 2 with Standard Scaler (cenário 03)."""
+    scenario = load_scenario('03-polynomial-regression-degree2.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-3'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+    assert_experiment_run_name_set(postgres_engine, experiment_run_id)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_4_polynomial_regression_degree3_with_scaler(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.4 – Polynomial regression degree 3 with Standard Scaler (cenário 04)."""
+    scenario = load_scenario('04-polynomial-regression-degree3.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-4'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+    assert_experiment_run_name_set(postgres_engine, experiment_run_id)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_5_linear_regression_with_lags(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.5 – Linear Regression with lag_train/lag_val per variable (cenário 05)."""
+    scenario = load_scenario('05-linear-regression-with-lags.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-5'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+    assert_experiment_run_name_set(postgres_engine, experiment_run_id)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_6_linear_regression_nan_interpolation(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.6 – nan_treatment='linear interpolation' (cenário 06)."""
+    scenario = load_scenario('06-linear-regression-nan-interpolation.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-6'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_7_linear_regression_static_window_removal(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.7 – rem_static_win=true with default static_threshold (cenário 07)."""
+    scenario = load_scenario('07-linear-regression-static-window-removal.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-7'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_8_linear_regression_with_limits(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.8 – support_filters with min/max limits per variable (cenário 08)."""
+    scenario = load_scenario('08-linear-regression-with-limits.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-8'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_9_polynomial_degree2_scaler_and_lags(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.9 – Polynomial degree 2, Standard Scaler and lags (cenário 09)."""
+    scenario = load_scenario('09-polynomial-degree2-with-scaler-and-lags.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-9'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+    assert_experiment_run_name_set(postgres_engine, experiment_run_id)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_10_linear_regression_with_ar_opt_params(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.10 – Linear regression with include_ar in opt_params (cenário 10)."""
+    scenario = load_scenario('10-linear-regression-with-ar.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-10'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+    assert_experiment_run_name_set(postgres_engine, experiment_run_id)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_11_linear_regression_static_threshold_custom(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.11 – rem_static_win=true with custom static_threshold (cenário 11)."""
+    scenario = load_scenario('11-linear-regression-static-threshold-custom.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-11'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_12_alternate_date_format_dd_mm_yyyy(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.12 – DATA column and dd/MM/yyyy format CSV (cenário 12)."""
+    scenario = load_scenario('12-angular-test-date-format.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(
+        postgres_engine,
+        experiment_run_id,
+        file_name='training_data_dd_mm_yyyy.csv',
+    )
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-12'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+    assert_experiment_run_name_set(postgres_engine, experiment_run_id)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_13_alternate_csv_narrow_date_window(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.13 – Same alternate CSV as 12 with date window (cenário 13)."""
+    scenario = load_scenario('13-angular-test-double-date-column.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(
+        postgres_engine,
+        experiment_run_id,
+        file_name='training_data_dd_mm_yyyy.csv',
+    )
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-13'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_14_polynomial_with_support_filters(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.14 – Polynomial degree 4, Standard Scaler, line support filters (cenário 14)."""
+    scenario = load_scenario('14-angular-test-polynomial-support-filters.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-14'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_15_linear_regression_custom_target_column_name(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.15 – Target column is not named ``target``; report path uses target_variable."""
+    scenario = load_scenario('15-linear-regression-custom-target-column.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(
+        postgres_engine,
+        experiment_run_id,
+        file_name='training_data_custom_target.csv',
+    )
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-15'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+    assert_experiment_run_name_set(postgres_engine, experiment_run_id)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_16_naive_timestamp_header_column(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.16 – CSV uses ``Timestamp`` header; snake_case date_column/date_format."""
+    scenario = load_scenario('16-linear-regression-naive-timestamp-header.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(
+        postgres_engine,
+        experiment_run_id,
+        file_name='training_data_timestamp_naive.csv',
+    )
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-16'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+    assert_experiment_run_name_set(postgres_engine, experiment_run_id)
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_1_17_linear_regression_blank_timestamp_row_dropped(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.1.17 – CSV has one empty timestamp cell; row is dropped and training succeeds."""
+    scenario = load_scenario('17-linear-regression-blank-timestamp-row.json')
+    experiment_run_id = scenario['experiment_run_id']
+    insert_experiment_run(
+        postgres_engine,
+        experiment_run_id,
+        file_name='training_data_blank_timestamp_row.csv',
+    )
+
+    await start_and_await_workflow(
+        temporal_test_env.client,
+        TrainModel.run,
+        scenario,
+        make_workflow_id('test-s1-1-17'),
+    )
+
+    assert_experiment_status(postgres_engine, experiment_run_id, 'TRAINING_SUCCESS')
+    assert_experiment_run_name_set(postgres_engine, experiment_run_id)
+
+
+# ---------------------------------------------------------------------------
+# 1.2 – Error paths
+# ---------------------------------------------------------------------------
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_2_1_minio_file_not_found(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.2.1 – Training file does not exist in MinIO → TRAINING_ERROR."""
+    scenario = load_scenario('01-linear-regression-basic.json')
+    scenario = {**scenario, 'experiment_run_id': 2001, 'file_name': 'does_not_exist.csv'}
+    experiment_run_id = 2001
+    insert_experiment_run(postgres_engine, experiment_run_id)
+
+    with pytest.raises(Exception):
+        await start_and_await_workflow(
+            temporal_test_env.client,
+            TrainModel.run,
+            scenario,
+            make_workflow_id('test-s1-2-1'),
+        )
+
+    assert_experiment_error(
+        postgres_engine,
+        experiment_run_id,
+        expected_status='TRAINING_ERROR',
+        error_substr='does_not_exist',
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.integration
+async def test_scenario_1_2_2_experiment_run_id_not_in_db(
+    temporal_test_env: WorkflowEnvironment,
+    temporal_worker: Worker,
+    postgres_engine,
+):
+    """Scenario 1.2.2 – experiment_run_id row absent → update_experiment_run raises."""
+    scenario = load_scenario('01-linear-regression-basic.json')
+    scenario = {**scenario, 'experiment_run_id': 9999}
+    # Intentionally NOT inserting the row
+
+    with pytest.raises(Exception):
+        await start_and_await_workflow(
+            temporal_test_env.client,
+            TrainModel.run,
+            scenario,
+            make_workflow_id('test-s1-2-2'),
+        )
+
+    assert_no_experiment_row(postgres_engine, 9999)

git_requirements_mapping.txt

@@ -0,0 +1,2 @@
+sientia_do: git+ssh://git@github.com/Aignosi/sientia-dataops-library.git
+sientia_model: git+ssh://git@github.com/Aignosi/sientia-model-library.git

input-sample.json

@@ -0,0 +1,38 @@
+{
+  "experiment_run_id": 1001,
+  "variable_columns": ["feature_a", "feature_b"],
+  "target_variable": "target",
+  "bucket_name": "model-training",
+  "file_name": "training_data.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "Linear Regression",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "feature_a": 0,
+      "feature_b": 0
+    },
+    "lag_val": {
+      "feature_a": 0,
+      "feature_b": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "Standard Scaler"
+  },
+  "opt_params": {}
+}

input-sample.md

@@ -0,0 +1,76 @@
+##### Insert a new experiment run
+
+```sql
+-- Optional: remove a previous run with the same id
+DELETE FROM public.experiment_run WHERE experiment_run_id = 1001;
+```
+
+```sql
+INSERT INTO public.experiment_run
+(experiment_name, run_name, username, status, error_message, created_at,
+updated_at, bucket_name, file_name, request_data, orchestrator_response_data)
+VALUES(
+	'test-experiment-name',
+	'test-run-name',
+	'test-username',
+	'ORCHESTRATOR_WAITING_PROC',
+	null,
+	now(),
+	now(),
+	'model-training',
+	'training_data.csv',
+	'{"experiment_run_id":1001,"variable_columns":["feature_a","feature_b"],"target_variable":"target","bucket_name":"model-training","file_name":"training_data.csv","line_separator":",","decimal_separator":".","train_size":80,"shuffle":true,"model_name":"Linear Regression","model_type":"linear_regression","data_model_kwargs":{"lag_train":{"feature_a":0,"feature_b":0},"lag_val":{"feature_a":0,"feature_b":0},"nan_treatment":"drop"},"model_kwargs":{"degree":1,"scaler_name":"Standard Scaler"},"opt_params":{}}',
+  null
+);
+```
+
+##### Upload the input dataset to MinIO
+
+```bash
+mc cp input_dataset.csv suse/model-training/training-sample-dataset-1001.csv
+```
+
+##### Temporal input payload sample
+
+Keys match `TrainModelParams.from_dict` in `model_manager/utils/models/train_model_params.py`: every field passed to `_check_none` must be present, including **`date_column`**; `model_metadata` must be non-empty for `validate_business_rules()`. You may omit **`date_format`** (defaults to `yyyy-MM-dd HH:mm:ss`). Omit optional keys (`random_state`, `val_file_name`, `model_id`) when defaults or `None` apply.
+
+```json
+{
+  "experiment_run_id": 1001,
+  "variable_columns": ["feature_a", "feature_b"],
+  "target_variable": "target",
+  "bucket_name": "model-training",
+  "file_name": "training-sample-dataset-1001.csv",
+  "line_separator": ",",
+  "decimal_separator": ".",
+  "date_column": "timestamp",
+  "train_size": 80,
+  "shuffle": true,
+  "random_state": 42,
+  "model_name": "test-runtime-linear-regression-model",
+  "model_type": "linear_regression",
+  "data_model_kwargs": {
+    "lag_train": {
+      "feature_a": 0,
+      "feature_b": 0
+    },
+    "lag_val": {
+      "feature_a": 0,
+      "feature_b": 0
+    },
+    "nan_treatment": "drop",
+    "rem_static_win": false,
+    "static_threshold": null,
+    "start_date": null,
+    "end_date": null,
+    "support_filters": {},
+    "removed_intervals": []
+  },
+  "model_kwargs": {
+    "degree": 1,
+    "interaction_only": false,
+    "scaler_name": "Standard Scaler"
+  },
+  "opt_params": {}
+}
+```

input_dataset.csv

@@ -0,0 +1,11 @@
+timestamp,Counter,Rollout,Square
+2026-01-01 00:00:00,1.0,2.0,5.1
+2026-01-01 00:01:00,2.0,2.0,5.9
+2026-01-01 00:02:00,2.0,3.0,8.3
+2026-01-01 00:03:00,3.0,3.0,9.2
+2026-01-01 00:04:00,3.0,4.0,10.8
+2026-01-01 00:05:00,4.0,4.0,11.7
+2026-01-01 00:06:00,4.0,5.0,14.1
+2026-01-01 00:07:00,5.0,5.0,15.15
+2026-01-01 00:08:00,5.0,6.0,17.2
+2026-01-01 00:09:00,6.0,6.0,17.9

model-manager-plugin-store-migration-plan.md

@@ -0,0 +1,385 @@
+---
+tags:
+  - engineering
+  - sientia
+  - runtime-system
+  - model-manager
+  - plugin-store
+  - migration-plan
+created: 2026-03-02
+modified: 2026-03-02
+created_by: Vitor Pimentel
+modified_by: Vitor Pimentel
+status: draft
+---
+
+# Sientia Model Manager — PluginStore Migration Plan
+
+> Implementation plan for migrating `sientia-dataops-model-manager` to use the Sientia PluginStore for runtime installation and model retrieval, aligned with the runtime architecture described in `analytics.md`.
+
+## Summary
+
+1. [[#Objectives and Scope|Objectives and Scope]] — What this migration must achieve
+2. [[#Existing State Overview (model-manager)|Existing State Overview]] — Current responsibilities and coupling points
+3. [[#Requirements Mapping|Requirements Mapping]] — Functional and non-functional requirements
+4. [[#Target Architecture|Target Architecture]] — Desired runtime and model-loading architecture
+5. [[#Implementation Plan|Implementation Plan]] — Phased, detailed changes to apply
+6. [[#Testing Strategy|Testing Strategy]] — How to validate the new behavior
+7. [[#Rollout and Migration Strategy|Rollout and Migration Strategy]] — How to safely roll out and deprecate old paths
+8. [[#Potential Model Library Changes|Potential Model Library Changes]] — Expected impact on `sientia-model-library`
+9. [[#Related Documents|Related Documents]] — Cross-links to supporting documents
+
+---
+
+## Objectives and Scope
+
+The goal of this work is to evolve `sientia-dataops-model-manager` so that:
+
+- It **detects the runtime** before the worker starts, using the `RUNTIME` environment variable.
+- It **installs the selected runtime** using the PluginStore runtime interface from `sientia_model.model_repository.plugin_store`:
+  - `PluginStore.install_runtime(runtime_name)`.
+- It **uses PluginStore to obtain models from the store**, instead of constructing them from the local ML template / mlops library:
+  - Pipelines call `PluginStore.get_model(...)` to obtain `SientiaModel` instances.
+  - The previous “in-repo model implementation plus mlops library” path is removed.
+
+Out of scope:
+
+- Changing Temporal workflow semantics (queues, retry policies, etc.).
+- Replacing the existing MLflow-based tracking and reporting; these remain the responsibility of `ModelRepository` and the reporting utilities.
+
+---
+
+## Existing State Overview (model-manager)
+
+The `sientia-dataops-model-manager` application currently:
+
+- **Worker orchestration** (`model_manager/worker/worker.py`)
+  - Reads Temporal configuration from env (`TEMPORAL_HOST`, `TEMPORAL_NAMESPACE`, task queues).
+  - Sets up observability (Prometheus, metrics, Sientia logger).
+  - Builds connector configs for Postgres, MLflow, MinIO, MongoDB.
+  - Instantiates `Activities` and schedules, then starts Temporal workers.
+  - Does not validate or install any “runtime” concept before worker startup.
+
+- **Training pipeline**
+  - `Training` activity (`model_manager/activities/training.py`) coordinates:
+    - Parameter validation (`validate_train_params`).
+    - Training execution via `TrainingRepository`.
+    - Saving trained models and artifacts via `ModelRepository` to MLflow.
+  - `TrainingRepository` (`model_manager/utils/repository/training_repository.py`):
+    - Loads and preprocesses CSV data (via `DataPreprocessor`).
+    - Trains a local `LinearRegressionModel` defined in `model_manager.sientia.models`.
+    - Computes metrics and builds a `TrainModelResult` for downstream steps.
+  - `ModelRepository` (`model_manager/utils/repository/model_repository.py`):
+    - Uses `ModelServing` and `Reports` to:
+      - Generate reports and CSV artifacts.
+      - Log parameters, metrics and models into MLflow.
+      - Today, production models are identified using **stages** (for example `Production`) in the Model Registry; aliases like `@production` are not yet used.
+      - MLflow-related logic here overlaps conceptually with the MLflow interactions implemented in `sientia-dataops-laborious_temporal`, which motivates extracting a **shared MLflow repository** into `sientia-dataops-library` (see `mlflow-shared-repository-migration-plan`).
+
+- **Coupling to models and runtimes**
+  - Training is tightly coupled to internal classes (`DataPreprocessor`, `LinearRegressionModel`).
+  - No runtime installation; env assumed ready.
+  - The system does not yet use PluginStore’s `get_model` or `install_runtime` capabilities.
+
+**MLflow:** All MLflow operations (lookup, promotion, etc.) → [[mlflow-shared-repository-migration-plan|shared repository]]. Model Manager uses the interface; it does not implement these concepts.
+
+### Current vs Target — High-level Flow
+
+```mermaid
+flowchart LR
+    subgraph currentState [Current State — Model Manager]
+        direction TB
+        WorkerMM["Temporal Worker\n(model_manager/worker.py)"]
+        ActivitiesMM["Activities\n(training, cleanup, etc.)"]
+        TrainRepo["TrainingRepository\n(local DataPreprocessor + LinearRegressionModel)"]
+        ModelRepoMM["ModelRepository\n(MLflow + reports)"]
+
+        WorkerMM -->|"Temporal tasks"| ActivitiesMM
+        ActivitiesMM -->|"train_model activity"| TrainRepo
+        TrainRepo -->|"TrainModelResult"| ModelRepoMM
+        ModelRepoMM -->|"experiments, runs, artifacts"| MLflowMM["MLflow Server"]
+    end
+
+    subgraph targetState [Target State — Model Manager]
+        direction TB
+        WorkerMM2["Temporal Worker\n+ Runtime bootstrap\n(read RUNTIME, install runtime)"]
+        ActivitiesMM2["Activities\n(+ ModelProvider)"]
+        PluginStoreNode["PluginStore\n(Gitea model store)"]
+        SientiaWrapper["SientiaModel wrapper\n(from store.get_model)"]
+        ModelRepoMM2["ModelRepository\n(MLflow + reports)"]
+
+        WorkerMM2 -->|"install_runtime(RUNTIME)"| PluginStoreNode
+        WorkerMM2 -->|"Temporal tasks"| ActivitiesMM2
+        ActivitiesMM2 -->|"get_training_model"| PluginStoreNode
+        PluginStoreNode -->|"SientiaModel instance"| SientiaWrapper
+        ActivitiesMM2 -->|"call train(...) on wrapper"| SientiaWrapper
+        SientiaWrapper -->|"TrainModelResult-compatible data"| ModelRepoMM2
+        ModelRepoMM2 -->|"experiments, runs, artifacts"| MLflowMM2["MLflow Server"]
+    end
+```
+
+### Current vs Target — Training Hot Path (Code Sketch)
+
+Current hot path in `TrainingRepository.train` (simplified):
+
+```python
+data = load_data(uploaded_file, params.line_separator, params.decimal_separator)
+data = _ensure_date_column_parsed(data, params)
+data = self._configure_datetime_index(data, params)
+
+process_data = self._init_data_preprocessor(params)
+process_data.fit(data)
+data_view = process_data.transform(data)
+
+x_train, x_test, y_train, y_test = split_train_test(...)
+
+regr = LinearRegressionModel(
+    target_variable=params.target_variable,
+    variable_columns=params.variable_columns,
+    degree=params.degree,
+    interaction_only=params.interaction_only,
+)
+regr.fit(pd.concat([x_train, y_train], axis=1))
+```
+
+Target hot path with PluginStore + `SientiaModel` (conceptual):
+
+```python
+data = load_and_preprocess(uploaded_file, params)          # keep existing preprocessing
+train_df, val_df = build_train_val_splits(data, params)   # explicit train/val
+
+wrapper = model_provider.get_training_model(
+    model_name=params.model_name,
+    runtime=os.environ["RUNTIME"],
+    opt_params={"env": params.environment},
+    model_kwargs={},
+    data_model_kwargs={},
+)
+
+wrapper.train(
+    train_data=train_df,
+    val_data=val_df,
+    target=params.target_variable,
+)
+
+# From here, metrics and artifacts are computed based on wrapper outputs
+```
+
+---
+
+## Requirements Mapping
+
+### Functional Requirements
+
+| ID    | Requirement                                  | Description                                                                                          | Impacted Areas                                                                 |
+|-------|----------------------------------------------|------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
+| FR-01 | Runtime detection via env var                | Before starting workers, read `RUNTIME` env var and fail fast if missing or empty                   | `model_manager/worker/worker.py`                                               |
+| FR-02 | Runtime installation via PluginStore         | Install the runtime defined in `RUNTIME` using `PluginStore.install_runtime(runtime_name)`          | `worker.py`, `sientia_model.model_repository.plugin_store.PluginStore`        |
+| FR-03 | PluginStore-based model retrieval            | Use `PluginStore.get_model(...)` to obtain `SientiaModel` instances for training                    | `TrainingRepository` (or new adapter), `Training` activity                     |
+| FR-04 | Remove mlops-library-based construction      | Do not construct models directly from `model_manager.sientia.models`; remove the mlops path  | `TrainingRepository`, `model_manager.sientia.models`, dependency graph        |
+| FR-05 | Use shared MLflow repository                | Delegate all MLflow ops (runs, metrics, artifacts, production lookup) to `SientiaMLflowRepository`; keep reports and `TrainModelResult` in Model Manager | `ModelRepository`, [[mlflow-shared-repository-migration-plan]] |
+| FR-06 | Centralized PluginStore configuration       | Configure PluginStore (Gitea URL, repo, auth, PyPI mirror) via env/config | Config helper, `worker.py`                                    |
+
+### Non-Functional Requirements
+
+| ID     | Requirement                              | Description                                                                                                       | Impacted Areas                                             |
+|--------|------------------------------------------|-------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------|
+| NFR-01 | Robust runtime bootstrap                 | If runtime installation fails, the worker must not start; errors must be explicit in logs/metrics                 | `worker.py`, PluginStore error handling                    |
+| NFR-02 | Single path                          | Use PluginStore + SientiaModel path only; no toggling or parallel old path | `worker.py`, Training activities                         |
+| NFR-03 | Observability and debuggability          | Logs and metrics must cover runtime detection, install attempts and PluginStore interactions                      | Logging around runtime and PluginStore                     |
+| NFR-04 | Testability                              | Unit and integration tests must cover runtime detection, install, and model retrieval                             | `tests/worker`, `tests/activities`, new PluginStore tests |
+| NFR-05 | Security                                 | No credentials hard-coded in source; rely on env/secret management                                                | Config helpers, deployment manifests                       |
+
+---
+
+## Target Architecture
+
+### Runtime bootstrap
+
+- **New required env var:** `RUNTIME` (name of the runtime; must match a runtime in the store index).
+- **New PluginStore configuration env vars** (names to be finalized):
+  - `STORE_BASE_URL`, `STORE_OWNER`, `STORE_REPO`
+  - Optional: `STORE_BRANCH`, `STORE_USERNAME`, `STORE_PASSWORD`, `PYPI_INDEX_URL`, `PYPI_USERNAME`, `PYPI_PASSWORD`
+- **Worker startup sequence** in `main()`:
+  1. Initialize logger and basic metadata as today.
+  2. Read `RUNTIME` from env; if missing/empty → log critical error and exit.
+  3. Build `PluginStore` instance using configuration env vars.
+  4. Call `store.install_runtime(runtime_name=RUNTIME)`; on failure → log error, set `APP_UP` metric to 0 and exit.
+  5. Only after successful runtime installation → build connector configs, instantiate Activities, connect to Temporal.
+
+### PluginStore usage in training
+
+- **ModelProvider abstraction** (e.g. `model_manager/utils/model_provider.py`):
+  - Holds a `PluginStore` instance and logger.
+  - Exposes `get_training_model(model_name, runtime, opt_params, model_kwargs, data_model_kwargs) -> SientiaModel` via `store.get_model(...)`.
+- **TrainingRepository integration:**
+  - Prepare input data using existing preprocessing logic.
+  - Ask ModelProvider for a `SientiaModel` instance (`model_name` from params, `runtime` from `RUNTIME`).
+  - Call the public `train(...)` method of the wrapper; collect outputs and metadata only through the public API.
+  - Build `TrainModelResult` from train/test splits, predictions, and artifacts required by `ModelRepository`.
+  - Model behavior lives in `SientiaModel`; manager focuses on orchestration and reports.
+
+---
+
+## Implementation Plan
+
+### Phase 0 — Design Alignment
+
+- **P0-01**: Confirm with stakeholders:
+  - The expected values and semantics for `RUNTIME` (naming convention, mapping to store runtimes).
+  - Whether the manager will ever need to support more than one runtime per process (current assumption: no).
+- **P0-02**: Decide the config strategy:
+  - Pure environment variables vs a config file + env overrides.
+- **P0-03**: Validate how `model_name` will be passed into the training workflow:
+  - Confirm or extend `TrainModelParams` to carry `model_name` and runtime-related fields as needed.
+
+### Phase 1 — Runtime Detection and Installation
+
+- **P1-01**: Extend `worker.py` docs and configuration:
+  - Add `RUNTIME` to the environment variables list in the module docstring.
+  - Document failure behavior when `RUNTIME` is missing or empty.
+- **P1-02**: Implement `build_plugin_store_from_env` helper:
+  - New function that:
+    - Reads `STORE_*` and `PYPI_*` env vars.
+    - Creates and returns a `PluginStore` instance with appropriate logger.
+  - Place it either in `worker.py` or in a dedicated utility module (e.g. `model_manager/utils/plugin_store_config.py`).
+- **P1-03**: Integrate runtime installation into `main()`:
+  - Before any Temporal client initialization:
+    - Read `runtime_name = os.getenv("RUNTIME")`.
+    - Build PluginStore via the helper.
+    - Call `install_runtime(runtime_name)`.
+  - Log:
+    - Start and end of runtime installation.
+    - List of installed requirements (name and version).
+  - On failure:
+    - Emit a clear message (including runtime name and store repo).
+    - Mark the app as DOWN in metrics.
+    - Exit with non-zero status.
+
+### Phase 2 — ModelProvider and Training Integration
+
+- **P2-01**: Introduce `ModelProvider` abstraction:
+  - Implement `ModelProvider` with:
+    - A reference to the shared `PluginStore`.
+    - Methods for retrieving models for training:
+      - `get_training_model(model_name, runtime, opt_params, model_kwargs, data_model_kwargs)`.
+  - Ensure it logs:
+    - Model and runtime names.
+    - Cache hits/misses when appropriate.
+- **P2-02**: Wire ModelProvider into Activities:
+  - Update the construction of `Activities` in `worker.py` to accept:
+    - A `ModelProvider` or a `PluginStore` that can be wrapped inside the `Training` activity.
+  - Update `Training.__init__` signature to accept this new dependency and store it as an attribute.
+- **P2-03**: Adjust `TrainingRepository` to use `SientiaModel`:
+  - In `train`:
+    1. Keep or refine current CSV loading and preprocessing pipeline (DataPreprocessor, split into train/test).
+    2. Use `ModelProvider` to retrieve the model:
+       - `model_name` from `TrainModelParams`.
+       - `runtime` from `RUNTIME`.
+       - `opt_params` mirroring how `test_plugin_store.py` interacts with models.
+    3. Call the model’s `train` method and produce the same core artifacts the current code expects:
+       - Train/test splits.
+       - Metrics and predictions for `TrainModelResult`.
+  - In `after_train_calculation`:
+    - Either re-use metrics from the SientiaModel, or continue calculating MSE/MAE/R² as a verification step.
+
+### Phase 3 — Removing mlops Dependencies
+
+- **P3-01**: Identify all usage points of:
+  - `LinearRegressionModel`.
+  - `DataPreprocessor` where behavior overlaps with what SientiaModel already does.
+- **P3-02**: Remove mlops-based code:
+  - Remove unused mlops-library-based training hooks.
+  - Simplify `TrainingRepository` to delegate as much as possible to SientiaModel logic.
+  - Use PluginStore + `SientiaModel` path only (no migration flag or parallel old path).
+
+### Phase 4 — ModelRepository, Shared MLflow Repository and Reporting Alignment
+
+- **P4-01**: Ensure `TrainModelResult` is correctly populated:
+  - Confirm that:
+    - `x_train`, `x_test`, `y_train`, `y_test`, `y_pred`, `y_train_pred` are provided by the new flow.
+    - Any fields required by `_generate_report` and `_save_run` remain available.
+- **P4-02**: Delegate all MLflow operations to `SientiaMLflowRepository` (see [[mlflow-shared-repository-migration-plan]]); keep in Model Manager only reporting orchestration and `TrainModelResult` construction.
+- **P4-03**: Validate that MLflow reports remain consistent:
+  - Run a side-by-side comparison between:
+    - A run from the previous (mlops-based) pipeline.
+    - A PluginStore-based run for the same dataset/experiment using the shared repository.
+  - Compare:
+    - Logged parameters.
+    - Metrics.
+    - Artifacts (reports, CSVs, equation JSON).
+
+---
+
+## Testing Strategy
+
+- **T1 — Unit tests**
+  - Worker: Test behavior when `RUNTIME` is missing or empty; test that `install_runtime` is called with the correct runtime name (mock PluginStore).
+  - ModelProvider: Test that it calls `PluginStore.get_model` with the expected arguments.
+  - TrainingRepository: Test that it uses the model returned by PluginStore instead of `LinearRegressionModel`.
+- **T2 — Integration tests**
+  - Set up a test store with a minimal model and runtime.
+  - Run a full training workflow: verify runtime installation first; confirm model is retrieved and training completes; validate metrics and artifacts via MLflow.
+- **T3 — Regression tests**
+  - Run the same experiment once with the old pipeline and once with the PluginStore-based pipeline.
+  - Compare key results (metrics and artifacts) to ensure differences are understood and acceptable.
+
+---
+
+## Rollout and Migration Strategy
+
+- **R1 — Criar modelos com a nova arquitetura**
+  - Garantir que o pipeline de modelagem (Factory/Warehouse/Store) consiga produzir modelos:
+    - Encapsulados em wrappers que estendem `SientiaModel`.
+    - Com interface estável para `train`, `retrain` (quando existir), `predict`/`transform` e `store_model`.
+  - Publicar um conjunto inicial de modelos “pilot” no store que será consumido pelo Model Manager.
+
+- **R2 — Subir um ou mais runtimes para esses modelos**
+  - Configurar e instalar runtimes específicos para os novos modelos, alinhados ao `RUNTIME` esperado pelo Model Manager:
+    - Verificar que cada runtime contém todas as dependências necessárias (via PluginStore / runtime installer).
+    - Validar que, em um ambiente de teste, o runtime consegue:
+      - Instalar bibliotecas.
+      - Carregar o wrapper via PluginStore e executar pelo menos um ciclo de treino de ponta a ponta.
+
+- **R3 — Colocar os novos modelos para rodar no Model Manager**
+  - Integrar o uso de `PluginStore.get_model(...)` na pipeline de treino:
+    - Direcionar um subconjunto de fluxos de treinamento para os modelos “pilot” oriundos do store.
+    - Monitorar:
+      - Estabilidade dos workers.
+      - Tempo de treino e consumo de recursos.
+      - Artefatos e métricas geradas no MLflow.
+  - Quando estáveis, coordenar com as equipes de produto/negócio para considerar esses modelos como candidatos a produção e, quando já estiverem usando MLflow 3+ com wrappers, promover esses modelos para produção usando aliases (`@production`).
+
+- **R4 — Migrar progressivamente os demais modelos**
+  - Definir uma ordem de migração por domínio/família de modelo (por exemplo: modelos de predição de série temporal, modelos de classificação, etc.):
+    - Para cada modelo legado:
+      - Criar/ajustar o wrapper `SientiaModel` correspondente no Factory/Warehouse.
+      - Garantir que o modelo passe a ser entregue pelo store e consumido via PluginStore.
+      - Executar o ciclo de testes (T1–T3) descrito na seção anterior.
+  - Após migrar todas as famílias de modelo:
+    - Remover o caminho de código que instancia diretamente `LinearRegressionModel` e demais classes locais.
+    - Limpar variáveis de configuração relacionadas à pipeline antiga (mlops library local).
+    - Assumir como padrão único: Model Manager treinando apenas modelos vindos do store, via wrappers `SientiaModel` e, quando aplicável, com resolução de produção por aliases em MLflow 3+.
+
+---
+
+## Potential Model Library Changes
+
+- **Clarifying the SientiaModel training interface:**
+  - Ensure that `SientiaModel` provides a stable way to train (including validation split handling).
+  - A clear contract for returning predictions and metrics.
+  - Access to any internal state needed for `TrainModelResult` construction.
+- **Improving PluginStore ergonomics:**
+  - Optionally add a helper to build `PluginStore` from environment variables (reusable across applications).
+  - More structured exceptions for missing runtime definitions, missing models, network and authentication issues.
+- These changes should be coordinated so that model manager and any other consumers can rely on a consistent, documented behavior.
+
+---
+
+## Related Documents
+
+- [[mlflow-shared-repository-migration-plan|MLflow Shared Repository Migration Plan]] — Concepts implemented in the common interface (production lookup, wrapper loading, promotion, etc.)
+- [[analytics-implementation-plan|Runtime Analytics Helm Implementation Plan]]
+- [[analytics|Runtime Analytics Architecture and Analysis]]
+- [[../model-plugin-system/06-end-to-end-flow|Model Plugin System — End-to-End Flow]]
+

model_manager/activities/activities.py

@@ -0,0 +1,166 @@
+from temporalio import workflow
+
+with workflow.unsafe.imports_passed_through():
+    from typing import Any
+
+    from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
+    from sientia_do.observability.logger import Logger
+    from sientia_do.observability.metrics_controller import MetricsController
+    from sientia_do.observability.sientia_monitoring import SientiaMonitoring
+    from sientia_do.repository.minio_repository_sync import MinioRepository
+    from sientia_model.model_repository.mlflow_repository import SientiaMLflowRepository
+    from sientia_model.model_repository.plugin_store import PluginStore
+
+    from model_manager.activities.cleanup import Cleanup
+    from model_manager.activities.experiment_tracking import ExperimentTracking
+    from model_manager.activities.training import Training
+
+
+class Activities(ExperimentTracking, Training, Cleanup):
+    """
+    Main activities orchestrator for the Model Manager system.
+
+    This class combines functionality from multiple activity classes to provide
+    a unified interface for all workflow operations. It manages database connections,
+    MLFlow model interactions, MinIO storage operations, and cleanup operations.
+
+    The class implements multiple inheritance to combine specialized functionality:
+    - ExperimentTracking: ML experiment lifecycle tracking and database operations
+    - Training: ML model training operations with MLFlow and MinIO integration
+    - Cleanup: File and directory cleanup operations for MinIO and local filesystem
+
+    Attributes:
+        postgres_config (dict): PostgreSQL connection configuration
+        mlflow_config (dict): MLFlow server configuration
+        minio_config (dict): MinIO storage configuration
+        logger (Logger): Logging and observability instance
+        notification_handler (NotificationHandler): Notification management instance
+    """
+
+    def __init__(
+        self,
+        postgres_config: dict[str, Any],
+        mlflow_config: dict[str, Any],
+        minio_config: dict[str, Any],
+        plugin_store: PluginStore,
+        logger: Logger,
+        notification_handler: NotificationHandler,
+        metrics_controller: MetricsController,
+    ):
+        """
+        Initialize the Activities orchestrator with all required configurations.
+
+        This constructor initializes all parent classes with their respective
+        configurations and sets up the foundation for all activity operations.
+
+        Args:
+            postgres_config: PostgreSQL connection configuration dictionary
+                Required keys: host, port, user, password, dbname, min_connections, max_connections
+            mlflow_config: MLFlow server configuration dictionary
+                Required keys: host, port, username, password
+            minio_config: MinIO storage configuration dictionary
+                Required keys: endpoint_url, access_key, secret_key, region, use_ssl, default_bucket
+            logger: Logger instance for observability and debugging
+            notification_handler: Notification handler for alerts and monitoring
+
+        Raises:
+            Exception: If any parent class initialization fails
+        """
+
+        ExperimentTracking.__init__(
+            self,
+            host=postgres_config['host'],
+            port=postgres_config['port'],
+            user=postgres_config['user'],
+            password=postgres_config['password'],
+            dbname=postgres_config['dbname'],
+            min_connections=postgres_config['min_connections'],
+            max_connections=postgres_config['max_connections'],
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+        )
+
+        self.mlflow_repository = SientiaMLflowRepository(
+            host=mlflow_config['url'],
+            username=mlflow_config['username'],
+            password=mlflow_config['password'],
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+        )
+
+        # MinIO repository used for all object storage operations
+        endpoint_url = minio_config['endpoint_url']
+        # MinioRepository expects the endpoint without scheme
+        if endpoint_url.startswith('http://'):
+            endpoint = endpoint_url.removeprefix('http://')
+        elif endpoint_url.startswith('https://'):
+            endpoint = endpoint_url.removeprefix('https://')
+        else:
+            endpoint = endpoint_url
+
+        self.minio_repository = MinioRepository(
+            endpoint=endpoint,
+            access_key=minio_config['access_key'],
+            secret_key=minio_config['secret_key'],
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+            secure=minio_config['use_ssl'],
+            bucket=minio_config['default_bucket'],
+        )
+
+        Training.__init__(
+            self,
+            mlflow_repository=self.mlflow_repository,
+            plugin_store=plugin_store,
+            minio_repository=self.minio_repository,
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+        )
+
+        Cleanup.__init__(
+            self,
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+        )
+
+    def __del__(self):
+        """
+        Destructor to safely handle cleanup during garbage collection.
+
+        This prevents AttributeError when the parent Postgres.__del__ tries to access
+        self.engine in objects with multiple inheritance. Only attempts cleanup if
+        the engine attribute exists.
+        """
+        # Only call parent __del__ if engine attribute exists
+        # This prevents AttributeError in multiple inheritance scenarios
+        if hasattr(self, 'engine'):
+            try:
+                # Call parent class __del__ if it exists
+                if hasattr(super(), '__del__'):  # pragma: no cover
+                    super().__del__()  # pragma: no cover
+            except Exception:  # noqa: S110, BLE001  # pragma: no cover
+                # Silently ignore errors during garbage collection
+                # Logging here could cause issues if logger is already destroyed
+                pass
+
+    def shutdown(self):
+        """
+        Gracefully shutdown all activities and clean up resources.
+
+        This method ensures proper cleanup of all resources including:
+        - PostgreSQL connection pools (via ExperimentTracking)
+        - Any other resources that need explicit cleanup
+
+        The method should be called before the application terminates to ensure
+        proper resource cleanup and prevent resource leaks.
+
+        Prefer calling this method explicitly rather than relying on __del__.
+        """
+        ExperimentTracking.close(self)
+        self.info('Postgres client closed')
+        SientiaMonitoring.shutdown(self)

model_manager/activities/cleanup.py

@@ -0,0 +1,177 @@
+"""
+Cleanup activities for removing stale files from local filesystem.
+
+This module provides activities for cleaning up temporary files and directories
+that are older than the configured retention period. It operates independently
+of the database, using timestamps embedded in filenames.
+"""
+
+from temporalio import activity, workflow
+
+with workflow.unsafe.imports_passed_through():
+    import os
+    import re
+    import shutil
+    import traceback
+    from datetime import datetime, timedelta
+    from typing import Any
+
+    from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
+    from sientia_do.notifications.models import NotificationLevel
+    from sientia_do.observability.logger import Logger
+    from sientia_do.observability.metrics_controller import MetricsController
+    from sientia_do.observability.sientia_monitoring import SientiaMonitoring
+
+    from model_manager.runtime_paths import REPORTS_TEMP_DIR
+
+    RETENTION_HOURS = int(os.getenv('CLEANUP_RETENTION_HOURS', '24'))
+    DRY_RUN = os.getenv('CLEANUP_DRY_RUN', 'false').lower() == 'true'
+
+
+class Cleanup(SientiaMonitoring):
+    """
+    Activity for cleaning up stale files and directories.
+
+    This activity extends SientiaMonitoring and handles cleanup of:
+    - Local temporary directories with timestamp suffixes
+    """
+
+    def __init__(
+        self,
+        logger: Logger,
+        notification_handler: NotificationHandler,
+        metrics_controller: MetricsController,
+    ):
+        """
+        Initialize Cleanup activity.
+
+        Args:
+            logger: Logger instance for observability
+            notification_handler: Handler for sending notifications
+            metrics_controller: Controller for metrics emission
+        """
+        SientiaMonitoring.__init__(self, logger, notification_handler, metrics_controller)
+
+        # Configuration from environment variables
+        self.retention_hours = RETENTION_HOURS
+        self.dry_run = DRY_RUN
+
+        # Regex patterns for timestamp extraction
+        self.dir_timestamp_pattern = re.compile(
+            r'^(.+)_(\d{8}_\d{6}_\d{6})$'
+        )  # name_YYYYMMDD_HHMMSS_microseconds
+
+    @activity.defn(name='cleanup_temp_directories')
+    def cleanup_temp_directories(self, input_data: dict[str, Any]) -> None:
+        """
+        Clean up stale temporary directories based on timestamp in directory name.
+
+        This activity scans the reports/temp directory for subdirectories following
+        the pattern '{name}_{timestamp}' where timestamp is in YYYYMMDD_HHMMSS_microseconds format.
+        Directories older than the retention period are deleted.
+
+        Args:
+            input_data: Cleanup configuration containing:
+                - metadata (dict): Workflow execution metadata
+                - temp_path (str): Path to temp directory (optional, defaults to reports/temp)
+
+        Returns:
+            None: Results are logged and tracked via metrics
+
+        Raises:
+            Exception: If cleanup fails (after sending notification)
+        """
+        metadata = input_data.get('metadata', {})
+        temp_path = input_data.get('temp_path', REPORTS_TEMP_DIR)
+
+        cutoff_time = datetime.now() - timedelta(hours=self.retention_hours)
+
+        try:
+            self.info(
+                f'Starting local directory cleanup - Path: {temp_path}, '
+                f'Retention: {self.retention_hours}h, Dry run: {self.dry_run}',
+                metadata,
+            )
+
+            if not os.path.exists(temp_path):
+                self.warning(f'Temp directory does not exist: {temp_path}', metadata)
+                return
+
+            directories_scanned = 0
+            directories_deleted = 0
+            errors = []
+
+            for item_name in os.listdir(temp_path):
+                item_path = os.path.join(temp_path, item_name)
+
+                if not os.path.isdir(item_path):
+                    continue
+
+                directories_scanned += 1
+
+                # Extract timestamp from directory name
+                match = self.dir_timestamp_pattern.match(item_name)
+                if not match:
+                    self.debug(
+                        f'Skipping directory without timestamp pattern: {item_name}', metadata
+                    )
+                    continue
+
+                timestamp_str = match.group(2)
+                try:
+                    # Parse YYYYMMDD_HHMMSS_microseconds
+                    dir_time = datetime.strptime(timestamp_str, '%Y%m%d_%H%M%S_%f')
+
+                    if dir_time < cutoff_time:
+                        age_hours = (datetime.now() - dir_time).total_seconds() / 3600
+
+                        if self.dry_run:
+                            self.info(
+                                f'[DRY RUN] Would delete directory: {item_name} (age: {age_hours:.1f}h)',
+                                metadata,
+                            )
+                            directories_deleted += 1
+                        else:
+                            try:
+                                shutil.rmtree(item_path)
+                                self.info(
+                                    f'Deleted stale directory: {item_name} (age: {age_hours:.1f}h)',
+                                    metadata,
+                                )
+                                directories_deleted += 1
+                            except OSError as e:
+                                error_msg = f'Failed to delete directory {item_name}: {str(e)}'
+                                errors.append(error_msg)
+                                self.error(error_msg, metadata)
+                    else:
+                        age_hours = (datetime.now() - dir_time).total_seconds() / 3600
+                        self.debug(
+                            f'Keeping recent directory: {item_name} (age: {age_hours:.1f}h)',
+                            metadata,
+                        )
+
+                except ValueError as e:
+                    error_msg = f'Invalid timestamp format in directory {item_name}: {str(e)}'
+                    errors.append(error_msg)
+                    self.error(error_msg, metadata)
+
+            self.info(
+                f'Directory cleanup completed - Scanned: {directories_scanned}, '
+                f'Deleted: {directories_deleted}, Errors: {len(errors)}',
+                metadata,
+            )
+
+        except Exception as e:
+            error_msg = f'Error in directory cleanup: {str(e)}'
+            trace = traceback.format_exc()
+
+            self.send_notification(
+                metadata=metadata,
+                notification_id='CLEANUP_DIRECTORIES_ERROR',
+                message=error_msg,
+                block='cleanup_temp_directories',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+
+            raise

model_manager/activities/experiment_tracking.py

@@ -0,0 +1,280 @@
+"""
+Experiment tracking activities for managing ML experiment lifecycle.
+
+This module provides activities for tracking and updating experiment run status
+in the PostgreSQL database, extending the synchronous Postgres client with specialized
+methods for experiment management.
+"""
+
+import enum
+
+from temporalio import activity, workflow
+
+with workflow.unsafe.imports_passed_through():
+    import traceback
+    from collections.abc import Mapping
+    from datetime import UTC, datetime
+    from typing import Any
+
+    from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
+    from sientia_do.notifications.models import NotificationLevel
+    from sientia_do.observability.logger import Logger
+    from sientia_do.observability.metrics_controller import MetricsController
+    from sientia_do.temporal.activities.postgres_sync import Postgres
+    from sqlalchemy import text
+
+
+class UpdateType(enum.StrEnum):
+    """Types of experiment run updates."""
+
+    STATUS = 'status'
+    STATUS_WITH_ERROR = 'status_with_error'
+    MODEL_SAVED = 'model_saved'
+
+
+class ExperimentTracking(Postgres):
+    """
+    Activity for tracking ML experiment lifecycle and status updates.
+
+    This activity extends the Postgres activity to provide specialized methods
+    for managing experiment runs, including status updates, error tracking, and
+    model registration. It maintains the experiment lifecycle from initialization
+    through training, model saving, and cleanup.
+
+    The activity uses a SQLAlchemy engine / connection pool and adds experiment-specific
+    operations with proper error handling and notifications.
+    """
+
+    def __init__(
+        self,
+        host: str,
+        port: int,
+        user: str,
+        password: str,
+        dbname: str,
+        min_connections: int,
+        max_connections: int,
+        logger: Logger,
+        notification_handler: NotificationHandler,
+        metrics_controller: MetricsController,
+    ):
+        """
+        Initialize ExperimentTracking activity with database configuration.
+
+        Args:
+            host: PostgreSQL server hostname
+            port: PostgreSQL server port
+            user: Database user
+            password: Database password
+            dbname: Database name
+            min_connections: Minimum connections in pool
+            max_connections: Maximum connections in pool
+            logger: Logger instance for observability
+            notification_handler: Notification handler for alerts
+            metrics_controller: Metrics controller for observability
+
+        Raises:
+            ConnectionError: If database connection cannot be established
+        """
+        Postgres.__init__(
+            self,
+            host=host,
+            port=port,
+            user=user,
+            password=password,
+            dbname=dbname,
+            min_connections=min_connections,
+            max_connections=max_connections,
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+        )
+
+        self.info(f'Postgres client initialized at {host}:{port}')
+
+    def __del__(self):
+        """
+        Destructor to safely handle cleanup during garbage collection.
+
+        This prevents AttributeError when used in multiple inheritance scenarios
+        where the parent Postgres.__del__ might be called on objects without
+        the engine attribute.
+        """
+        # Only call parent __del__ if engine attribute exists
+        if hasattr(self, 'engine'):
+            try:
+                if hasattr(super(), '__del__'):
+                    super().__del__()
+            except Exception:  # noqa: S110, BLE001
+                # Silently ignore errors during garbage collection
+                pass
+
+    def _execute_update(self, query: str, params: Mapping[str, Any]) -> dict[str, Any]:
+        """
+        Execute an UPDATE SQL statement.
+
+        Args:
+            query: Parameterized SQL string to execute.
+            params: Mapping of parameters for the SQL query.
+
+        Returns:
+            dict: A dictionary containing the affected row count: {'rowcount': int}.
+        """
+
+        with self.engine.begin() as connection:
+            result = connection.execute(text(query), params)
+            return {'rowcount': result.rowcount}
+
+    def _build_status_update_query(
+        self, status: str | None, experiment_run_id: int
+    ) -> tuple[str, dict[str, Any]]:
+        """Build SQL query for simple status update."""
+        if not isinstance(status, str) or not status:
+            raise ValueError('status is required for STATUS update type')
+
+        sql_query = """
+            UPDATE experiment_run
+            SET status = :status, updated_at = :updated_at
+            WHERE id = :experiment_run_id
+        """
+
+        query_params = {
+            'status': status,
+            'updated_at': datetime.now(UTC),
+            'experiment_run_id': experiment_run_id,
+        }
+
+        return sql_query, query_params
+
+    def _build_status_with_error_query(
+        self, status: str | None, error_message: str | None, experiment_run_id: int
+    ) -> tuple[str, dict[str, Any]]:
+        """Build SQL query for status update with error message."""
+        if not isinstance(status, str) or not status:
+            raise ValueError('status is required for STATUS_WITH_ERROR update type')
+
+        if not isinstance(error_message, str) or not error_message:
+            raise ValueError('error_message is required for STATUS_WITH_ERROR update type')
+
+        # Truncate error message if too long
+        truncated_error = error_message[:1024] if len(error_message) > 1024 else error_message
+
+        sql_query = """
+            UPDATE experiment_run
+            SET status = :status, error_message = :error_message, updated_at = :updated_at
+            WHERE id = :experiment_run_id
+        """
+
+        query_params = {
+            'status': status,
+            'error_message': truncated_error,
+            'updated_at': datetime.now(UTC),
+            'experiment_run_id': experiment_run_id,
+        }
+
+        return sql_query, query_params
+
+    def _build_model_saved_query(
+        self, run_name: str | None, status: str | None, experiment_run_id: int
+    ) -> tuple[str, dict[str, Any]]:
+        """Build SQL query for model saved update."""
+        if not isinstance(run_name, str) or not run_name:
+            raise ValueError('run_name is required for MODEL_SAVED update type')
+
+        if not isinstance(status, str) or not status:
+            raise ValueError('status is required for MODEL_SAVED update type')
+
+        sql_query = """
+            UPDATE experiment_run
+            SET run_name = :run_name, status = :status, updated_at = :updated_at
+            WHERE id = :experiment_run_id
+        """
+
+        query_params = {
+            'run_name': run_name,
+            'status': status,
+            'updated_at': datetime.now(UTC),
+            'experiment_run_id': experiment_run_id,
+        }
+
+        return sql_query, query_params
+
+    def _get_update_query_and_params(
+        self, update_type: str, experiment_run_id: int, input_data: dict[str, Any]
+    ) -> tuple[str, dict[str, Any]]:
+        """Get SQL query and parameters based on update type."""
+        status = input_data.get('status')
+        error_message = input_data.get('error_message')
+        run_name = input_data.get('run_name')
+
+        if update_type == UpdateType.STATUS:
+            return self._build_status_update_query(status, experiment_run_id)
+
+        if update_type == UpdateType.STATUS_WITH_ERROR:
+            return self._build_status_with_error_query(status, error_message, experiment_run_id)
+
+        if update_type == UpdateType.MODEL_SAVED:
+            return self._build_model_saved_query(run_name, status, experiment_run_id)
+
+        raise ValueError(f'Invalid update_type: {update_type}')
+
+    @activity.defn(name='update_experiment_run')
+    def update_experiment_run(self, input_data: dict[str, Any]) -> None:
+        """
+        Update experiment run with status, errors, or model information.
+
+        This activity provides a unified interface for all experiment run updates,
+        supporting different update types through a single method. It automatically
+        selects the appropriate SQL query based on the update type and parameters.
+
+        Args:
+            input_data: Configuration for experiment run update operation
+                Required keys:
+                    - metadata (dict): Workflow execution metadata
+                    - experiment_run_id (int): Unique identifier for the experiment run
+                    - update_type (str): Type of update (status, status_with_error, model_saved)
+                Optional keys:
+                    - status (str): New status for the experiment run
+                    - error_message (str): Error message if update failed
+                    - run_name (str): MLFlow run name if model was saved
+
+        Raises:
+            ValueError: If required parameters are missing for the update type
+            RuntimeError: If update operation fails
+        """
+        metadata = input_data.get('metadata')
+        experiment_run_id = input_data['experiment_run_id']
+        update_type = input_data['update_type']
+        status = input_data.get('status')
+
+        try:
+            sql_query, query_params = self._get_update_query_and_params(
+                update_type, experiment_run_id, input_data
+            )
+
+            result = self._execute_update(sql_query, query_params)
+
+            if result.get('rowcount', 0) == 0:
+                error_msg = (
+                    f'No experiment_run row updated for id={experiment_run_id} '
+                    f'(row missing or id mismatch). update_type={update_type!r}, status={status!r}.'
+                )
+                raise ValueError(error_msg)
+
+            self.info(
+                f'Successfully updated experiment run {experiment_run_id} with status {status}',
+                metadata,
+            )
+        except Exception as e:  # noqa: BLE001
+            error_msg = f'Error updating experiment run - ID: {experiment_run_id}, Status: {status}, Error: {str(e)}'
+            trace = traceback.format_exc()
+
+            self.send_notification(
+                metadata=metadata or {},
+                notification_id='UPDATE_EXPERIMENT_RUN_ERROR',
+                message=error_msg,
+                block='update_experiment_run',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+            raise RuntimeError(error_msg) from e

model_manager/activities/training.py

@@ -0,0 +1,497 @@
+"""
+Training activities for ML model training operations.
+
+This module provides activities for training machine learning models.
+The activity extends BaseActivity and receives pre-downloaded files
+and raises `ModelTrainingError` when training fails.
+"""
+
+from sientia_model.wrappers.sientia_model import SientiaModel
+from temporalio import activity, workflow
+
+with workflow.unsafe.imports_passed_through():
+    import os
+    import time
+    import traceback
+    from typing import Any
+
+    import mlflow
+    from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
+    from sientia_do.notifications.models import NotificationLevel
+    from sientia_do.observability.logger import Logger
+    from sientia_do.observability.metrics_controller import MetricsController
+    from sientia_do.observability.sientia_monitoring import SientiaMonitoring
+    from sientia_do.repository.minio_repository_sync import MinioRepository
+    from sientia_model.model_repository.mlflow_repository import SientiaMLflowRepository
+    from sientia_model.model_repository.plugin_store import PluginStore
+
+    from model_manager import metrics as mm_metrics
+    from model_manager.utils.models.train_model_params import TrainModelParams
+    from model_manager.utils.models.train_model_result import TrainModelResult
+    from model_manager.utils.repository.data_manager_repository import DataManagerRepository
+
+
+class Training(SientiaMonitoring):
+    """
+    Activity for ML model training operations.
+
+    This activity extends SientiaMonitoring and handles machine learning model
+    training with comprehensive error handling. It receives pre-downloaded
+    files from the workflow and raises `ModelTrainingError` on failure so the
+    workflow can map the correct experiment status.
+    """
+
+    def __init__(
+        self,
+        mlflow_repository: SientiaMLflowRepository,
+        plugin_store: PluginStore,
+        minio_repository: MinioRepository,
+        logger: Logger,
+        notification_handler: NotificationHandler,
+        metrics_controller: MetricsController,
+    ):
+        """
+        Initialize Training activity.
+
+        Args:
+            logger: Logger instance for observability
+            notification_handler: Handler for sending notifications
+        """
+        SientiaMonitoring.__init__(self, logger, notification_handler, metrics_controller)
+        self.data_manager_repository = DataManagerRepository(logger)
+        self.mlflow_repository = mlflow_repository
+        self.plugin_store = plugin_store
+        self.minio_repository = minio_repository
+
+    @activity.defn(name='load_model_metadata')
+    def load_model_metadata(self, input_data: dict[str, Any]) -> dict[str, Any]:
+        """
+        Load model metadata/schemas from the model store.
+
+        This activity is responsible for fetching model metadata/schemas from the
+        model store index and extracting a serializable `model_metadata` dict that
+        `TrainModelParams.validate_business_rules()` depends on.
+
+        Args:
+            input_data: Workflow input at the same level as `validate_train_params`,
+                including at least `model_name` and the fields required by
+                `TrainModelParams.from_dict` to build wrapper kwargs.
+
+        Return:
+            dict[str, Any]: Updated `input_data` containing `input_data['model_metadata']`.
+        """
+        metadata = input_data.get('metadata', {})
+
+        self.info(f'Loading model metadata for {input_data}', metadata)
+
+        try:
+            train_params = TrainModelParams.from_dict(input_data)
+            model_metadata = self.plugin_store.get_model_index(
+                model_type=train_params.model_type,
+                metadata=metadata,
+            )
+
+            train_params.model_metadata = model_metadata
+            self.info(f'Model metadata loaded successfully for {input_data}', metadata)
+            self.debug(f'Model metadata: {model_metadata}', metadata)
+            return train_params.to_dict()
+        except Exception as exc:
+            trace = traceback.format_exc()
+            self.send_notification(
+                metadata=metadata,
+                notification_id='LOAD_MODEL_METADATA_ERROR',
+                message=f'Error loading model metadata: {str(exc)}',
+                block='load_model_metadata',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+            raise
+
+    @activity.defn(name='validate_train_params')
+    def validate_train_params(self, input_data: dict[str, Any]) -> dict[str, Any]:
+        """
+        Validate and convert training parameters from dict to TrainModelParams.
+
+        This activity validates the input training parameters and converts them
+        to a TrainModelParams object.
+
+        Args:
+            input_data: Training parameters and metadata at the same level
+                Required keys:
+                    - metadata (dict): Workflow execution metadata
+                    - All TrainModelParams fields (experiment_run_id, target_variable, etc.)
+
+        Returns:
+            dict[str, Any]: Validated and converted training parameters as dictionary
+
+        Raises:
+            Exception: If validation fails (after sending notification)
+        """
+        metadata = input_data.get('metadata', {})
+
+        self.info(f'Validating training parameters for {input_data}', metadata)
+
+        try:
+            train_params = TrainModelParams.from_dict(input_data)
+
+            train_params.validate_business_rules()
+
+            self.info(
+                f'Training parameters validated successfully - '
+                f'Target: {train_params.target_variable}, '
+                f'Experiment: {train_params.experiment_name}',
+                metadata,
+            )
+
+            self.debug(
+                f'Training parameters validated successfully: {train_params.to_dict()}', metadata
+            )
+
+            return train_params.to_dict()
+        except Exception as e:
+            error_msg = f'Error validating training parameters: {str(e)}'
+            trace = traceback.format_exc()
+
+            self.send_notification(
+                metadata=metadata,
+                notification_id='VALIDATE_TRAIN_PARAMS_ERROR',
+                message=error_msg,
+                block='validate_train_params',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+            raise
+
+    @activity.defn(name='train_model')
+    def train_model(self, input_data: dict[str, Any]) -> dict[str, Any]:
+        """
+        Train a machine learning model.
+
+        This activity orchestrates the ML training pipeline:
+        1. Validate input parameters.
+        2. Prepare data via DataManagerRepository.
+        3. Train the model and compute metrics.
+
+        Args:
+            input_data: Training configuration containing:
+                - metadata (dict): Workflow execution metadata.
+                - uploaded_file (BytesIO): Training data already downloaded from MinIO.
+                - train_params (dict): Training parameters.
+
+        Returns:
+            dict[str, Any]: Serializable summary (run identifiers, run_dir for cleanup, regression metrics).
+
+        Raises:
+            ValueError: If input validation fails.
+            Exception: If training fails (after sending notification).
+        """
+        metadata = input_data.get('metadata')
+        train_params = TrainModelParams.from_dict(input_data['train_params'])
+        labels = self._get_training_labels(train_params)
+
+        self.info('Starting train_model process', metadata)
+
+        try:
+            # Download training file bytes from MinIO
+            self.info(
+                f'Downloading training file from MinIO for {train_params.file_name}', metadata
+            )
+            train_bytes = self.minio_repository.download_file(
+                object_name=train_params.file_name,
+                bucket=train_params.bucket_name,
+                metadata=metadata,
+            )
+
+            # Download optional validation file bytes from the same bucket
+            val_bytes: bytes | None = None
+            validation_name = train_params.val_file_name
+            if validation_name is not None:
+                self.info(f'Downloading validation file from MinIO for {validation_name}', metadata)
+                val_bytes = self.minio_repository.download_file(
+                    object_name=validation_name,
+                    bucket=train_params.bucket_name,
+                    metadata=metadata,
+                )
+
+            self.info(f'Preparing training data for {train_params.file_name}', metadata)
+            train_result = self._prepare_data(train_bytes, val_bytes, train_params, metadata)
+
+            mm_metrics.SIENTIA_TRAINING_DATASET_TRAIN_ROWS.labels(**labels).set(
+                len(train_result.train_data)
+            )
+            mm_metrics.SIENTIA_TRAINING_DATASET_VAL_ROWS.labels(**labels).set(
+                len(train_result.val_data)
+            )
+            mm_metrics.SIENTIA_TRAINING_FEATURE_COUNT.labels(**labels).set(
+                len(train_params.variable_columns)
+            )
+
+            self.info(f'Getting model wrapper for {train_params.model_type}', metadata)
+            wrapper = self.plugin_store.get_model(
+                model_type=train_params.model_type,
+                force_download=False,
+                opt_params=train_params.opt_params or {},
+                model_kwargs=train_params.model_kwargs or {},
+                data_model_kwargs=train_params.data_model_kwargs or {},
+                metadata=metadata,
+            )
+
+            if self.logger is not None:
+                wrapper.logger = self.logger.base_logger
+
+            self.info(f'Training model for {train_params.model_type}', metadata)
+            train_result = self._fit_model(wrapper, train_result, train_params, metadata)
+
+            self.info(f'Computing regression metrics for {train_params.model_type}', metadata)
+            train_result = self.data_manager_repository.compute_regression_metrics(
+                train_result,
+                wrapper,
+                metadata=metadata,
+            )
+
+            if train_result.mse_val is not None:
+                mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_MSE.labels(**labels).set(
+                    train_result.mse_val
+                )
+            if train_result.mae_val is not None:
+                mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_MAE.labels(**labels).set(
+                    train_result.mae_val
+                )
+            if train_result.r2_val is not None:
+                mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_R2.labels(**labels).set(
+                    train_result.r2_val
+                )
+
+            mm_metrics.SIENTIA_TRAINING_INFO.labels(
+                pod_id=labels['pod_id'],
+                model_name=train_params.model_name,
+                model_type=train_params.model_type,
+                dataset_train_rows=str(len(train_result.train_data)),
+                dataset_val_rows=str(len(train_result.val_data)),
+                feature_count=str(len(train_params.variable_columns)),
+                mse=str(train_result.mse_val) if train_result.mse_val is not None else '',
+                mae=str(train_result.mae_val) if train_result.mae_val is not None else '',
+                r2=str(train_result.r2_val) if train_result.r2_val is not None else '',
+            ).set(time.time() * 1000)
+
+            self.info(f'Starting MLflow run for {train_params.model_type}', metadata)
+            with self.mlflow_repository.start_run(
+                model_name=train_params.model_name,
+                run_name=train_result.run_name,
+                experiment_name=train_result.experiment_name,
+                tags=None,
+                metadata=metadata,
+            ) as run_info:
+                train_result.run_id = run_info.run_id
+                self._persist_training_artifacts(train_result, train_params, wrapper, metadata)
+
+            self.emit_metric_sync(
+                metric_object=mm_metrics.SIENTIA_TRAINING_MODEL_TRAINED_TOTAL,
+                tags=labels,
+            )
+
+            return {
+                'run_name': train_result.run_name,
+                'experiment_name': train_result.experiment_name,
+                'run_id': train_result.run_id,
+                'run_dir': train_result.run_dir,
+            }
+        except Exception as e:  # noqa: BLE001
+            error_msg = f'Error training model - error: {str(e)}'
+
+            trace = traceback.format_exc()
+
+            self.send_notification(
+                metadata=metadata or {},
+                notification_id='TRAIN_MODEL_ERROR',
+                message=error_msg,
+                block='train_model',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+
+            raise e
+
+    def _get_training_labels(self, train_params: TrainModelParams) -> dict:
+        return {
+            'pod_id': os.getenv('HOSTNAME', 'localhost'),
+            'model_name': train_params.model_name,
+            'model_type': train_params.model_type,
+        }
+
+    def _prepare_data(
+        self,
+        train_bytes: bytes,
+        val_bytes: bytes | None,
+        train_params: TrainModelParams,
+        metadata: dict | None,
+    ) -> TrainModelResult:
+        labels = self._get_training_labels(train_params)
+        start_time = time.time()
+        try:
+            return self.data_manager_repository.prepare_training_data(
+                train_file_bytes=train_bytes,
+                validation_file_bytes=val_bytes,
+                params=train_params,
+                metadata=metadata,
+            )
+        except Exception:
+            self.emit_metric_sync(
+                metric_object=mm_metrics.SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL,
+                tags=labels,
+            )
+            raise
+        finally:
+            self.observe_lag_sync(
+                start_time,
+                mm_metrics.SIENTIA_TRAINING_DATA_PREPARATION_LAG,
+                labels,
+            )
+
+    def _fit_model(
+        self,
+        wrapper: Any,
+        train_result: TrainModelResult,
+        train_params: TrainModelParams,
+        metadata: dict | None,
+    ) -> TrainModelResult:
+        labels = self._get_training_labels(train_params)
+        train_data = train_result.train_data
+        val_data = train_result.val_data
+
+        self.debug(
+            f'train_model prepared data (head 10):\ntrain:\n{train_data.head(10).to_string()}'
+            f'\nval:\n{val_data.head(10).to_string()}',
+            metadata,
+        )
+
+        start_time = time.time()
+        try:
+            wrapper.train(
+                train_data=train_data,
+                val_data=val_data,
+                target=train_params.target_variable,
+            )
+
+            self.info(
+                f'Generating predictions using the trained wrapper for {train_params.model_type}',
+                metadata,
+            )
+            transformed_train, _ = wrapper.transform(train_data)
+            transformed_val, _ = wrapper.transform(val_data)
+
+            self.debug(
+                f'train_model transform (head 10):\ntrain:\n{transformed_train.head(10).to_string()}'
+                f'\nval:\n{transformed_val.head(10).to_string()}',
+                metadata,
+            )
+
+            y_train_pred_df, _ = wrapper.predict({}, transformed_train)
+            y_val_pred_df, _ = wrapper.predict({}, transformed_val)
+
+            self.debug(
+                f'train_model predict (head 10):\ntrain:\n{y_train_pred_df.head(10).to_string()}'
+                f'\nval:\n{y_val_pred_df.head(10).to_string()}',
+                metadata,
+            )
+
+            y_train_pred_df.sort_index(inplace=True, ascending=False)
+            y_val_pred_df.sort_index(inplace=True, ascending=False)
+
+            train_result.y_train_pred = y_train_pred_df
+            train_result.y_pred = y_val_pred_df
+            return train_result
+        except Exception:
+            self.emit_metric_sync(
+                metric_object=mm_metrics.SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL,
+                tags=labels,
+            )
+            raise
+        finally:
+            self.observe_lag_sync(
+                start_time,
+                mm_metrics.SIENTIA_TRAINING_MODEL_FIT_LAG,
+                labels,
+            )
+
+    def _persist_training_artifacts(
+        self,
+        train_result: TrainModelResult,
+        train_params: TrainModelParams,
+        wrapper: SientiaModel,
+        metadata: dict[str, Any] | None,
+    ) -> None:
+        self.info(f'Generating report for {train_params.model_type}', metadata)
+        train_result = self.data_manager_repository.generate_report(
+            train_result,
+            metadata=metadata,
+        )
+
+        if (
+            train_result.report_path is None
+            or train_result.train_data_path is None
+            or train_result.test_data_path is None
+        ):
+            raise ValueError('Report path, train data path, or test data path is not set')
+
+        self.info(f'Storing model for {train_params.model_type}', metadata)
+        wrapper._input_example = None
+        wrapper.store_model(name=train_params.model_name)
+        self._log_regression_metrics_as_params(train_result)
+        self.info(f'Logging artifacts for {train_params.model_type}', metadata)
+        mlflow.log_artifact(train_result.report_path)
+        mlflow.log_artifact(train_result.train_data_path)
+        mlflow.log_artifact(train_result.test_data_path)
+        if train_result.equation_path is not None:
+            mlflow.log_artifact(train_result.equation_path)
+
+    def _log_regression_metrics_as_params(self, train_result: TrainModelResult) -> None:
+        """
+        Persist computed regression metrics as MLflow params.
+
+        Args:
+            train_result: Training output containing computed regression metrics.
+        """
+        metric_params = {
+            'mse_val': train_result.mse_val,
+            'mae_val': train_result.mae_val,
+            'r2_val': train_result.r2_val,
+        }
+
+        for key, value in metric_params.items():
+            if value is not None:
+                mlflow.log_param(key, value)
+
+    @activity.defn(name='cleanup_resources')
+    def cleanup_resources(self, input_data: dict[str, Any]) -> None:
+        """
+        Cleanup temporary resources created during training.
+
+        Args:
+            input_data: Cleanup configuration containing:
+                - metadata (dict): Workflow execution metadata.
+                - run_dir (str): Temporary directory to remove.
+
+        Raises:
+            Exception: If cleanup fails (after sending notification).
+        """
+        metadata = input_data.get('metadata', {})
+        run_dir = input_data.get('run_dir', '')
+
+        try:
+            self.data_manager_repository.cleanup_run_directory(run_dir, metadata)
+        except Exception as e:  # noqa: BLE001
+            error_msg = f'Error cleaning up resources - Run directory: {run_dir}, Error: {str(e)}'
+
+            trace = traceback.format_exc()
+
+            self.send_notification(
+                metadata=metadata,
+                notification_id='CLEANUP_RESOURCES_ERROR',
+                message=error_msg,
+                block='cleanup_resources',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+
+            raise

model_manager/metrics.py

@@ -0,0 +1,110 @@
+"""
+Model Manager Metrics Module
+
+This module defines all Prometheus metrics used by the Sientia DataOps Model Manager system
+for monitoring and observability. The metrics provide insights into system performance,
+training operations, and operational health.
+
+The metrics are designed to be scraped by Prometheus and can be visualized in
+Grafana or other monitoring dashboards to provide real-time visibility into
+the system's operation.
+
+Key Metric Categories:
+- Application Health: Overall system status and availability
+
+Metric Labels:
+- pod_id: Kubernetes pod identifier for multi-instance deployments
+"""
+
+from prometheus_client import Counter, Gauge, Histogram
+
+# Application health metric
+APP_UP = Gauge(
+    'app_up',
+    'Indicates if the application is running (1) or shutting down (0)',
+    ['pod_id'],
+)
+
+_TRAINING_LABELS = ['pod_id', 'model_name', 'model_type']
+
+SIENTIA_TRAINING_INFO = Gauge(
+    'sientia_training_info',
+    'Metadata and execution timestamp (ms) of the last successful model training run',
+    [
+        'pod_id',
+        'model_name',
+        'model_type',
+        'dataset_train_rows',
+        'dataset_val_rows',
+        'feature_count',
+        'mse',
+        'mae',
+        'r2',
+    ],
+)
+
+SIENTIA_TRAINING_MODEL_TRAINED_TOTAL = Counter(
+    'sientia_training_model_trained_total',
+    'Number of successfully completed model training runs',
+    _TRAINING_LABELS,
+)
+
+SIENTIA_TRAINING_DATA_PREPARATION_LAG = Histogram(
+    'sientia_training_data_preparation_lag',
+    'Latency of prepare_training_data() in seconds',
+    _TRAINING_LABELS,
+)
+
+SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL = Counter(
+    'sientia_training_data_preparation_error_count_total',
+    'Number of failures in prepare_training_data()',
+    _TRAINING_LABELS,
+)
+
+SIENTIA_TRAINING_MODEL_FIT_LAG = Histogram(
+    'sientia_training_model_fit_lag',
+    'Latency of wrapper.train() (model fitting) in seconds',
+    _TRAINING_LABELS,
+)
+
+SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL = Counter(
+    'sientia_training_model_fit_error_count_total',
+    'Number of failures in wrapper.train() (model fitting)',
+    _TRAINING_LABELS,
+)
+
+SIENTIA_TRAINING_MODEL_QUALITY_MSE = Gauge(
+    'sientia_training_model_quality_mse',
+    'Mean Squared Error of the last successful model training',
+    _TRAINING_LABELS,
+)
+
+SIENTIA_TRAINING_MODEL_QUALITY_MAE = Gauge(
+    'sientia_training_model_quality_mae',
+    'Mean Absolute Error of the last successful model training',
+    _TRAINING_LABELS,
+)
+
+SIENTIA_TRAINING_MODEL_QUALITY_R2 = Gauge(
+    'sientia_training_model_quality_r2',
+    'R-squared of the last successful model training',
+    _TRAINING_LABELS,
+)
+
+SIENTIA_TRAINING_DATASET_TRAIN_ROWS = Gauge(
+    'sientia_training_dataset_train_rows',
+    'Number of rows in the training dataset after preparation',
+    _TRAINING_LABELS,
+)
+
+SIENTIA_TRAINING_DATASET_VAL_ROWS = Gauge(
+    'sientia_training_dataset_val_rows',
+    'Number of rows in the validation dataset after preparation',
+    _TRAINING_LABELS,
+)
+
+SIENTIA_TRAINING_FEATURE_COUNT = Gauge(
+    'sientia_training_feature_count',
+    'Number of input feature columns used for training',
+    _TRAINING_LABELS,
+)

model_manager/reports/header.html

@@ -0,0 +1,167 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <link rel="stylesheet" href="style.css" />
+
+    <title>Report</title>
+
+    <style>
+      * {
+        font-family: "Franklin Gothic Medium", "Arial Narrow", Arial, sans-serif;
+        margin: 0;
+        padding: 0;
+        box-sizing: border-box;
+      }
+
+      .logo {
+        padding-top: 70;
+        padding-bottom: 70;
+        position: absolute;
+        margin-left: -48px;
+      }
+
+      h1 {
+        color: #fff;
+        position: absolute;
+        margin-left: 45%;
+      }
+
+      html,
+      body {
+        scroll-behavior: smooth;
+      }
+
+      section {
+        padding-top: 90px;
+        width: 100%;
+        display: fixed;
+        justify-content: center;
+        align-items: center;
+        background-color: rgb(217, 217, 214, 0.7);
+      }
+
+      .material-symbols-outlined {
+        font-variation-settings: "FILL" 0, "wght" 400, "GRAD" 0, "opsz" 24;
+        color: #ffff;
+      }
+
+      /* Tooltip text */
+      .tooltiptext {
+        visibility: hidden;
+        background-color: rgb(0, 30, 96, 0.9);
+        padding: 10px;
+        margin-left: -90px;
+        font-size: 16px;
+        position: absolute;
+        top: 85px;
+        border-bottom-left-radius: 12px;
+        border-bottom-right-radius: 12px;
+      }
+
+      /* Show the tooltip text when you mouse over the tooltip container */
+      .material-symbols-outlined:hover .tooltiptext {
+        visibility: visible;
+      }
+
+      header {
+        position: fixed;
+        top: 0;
+        width: 100%;
+        height: 85px;
+        background: rgb(0, 30, 96, 0.95);
+        display: flex;
+        justify-content: space-between;
+        align-items: center;
+        padding: 10px 50px 0;
+      }
+
+      header nav {
+        display: absolute;
+        margin-left: 80%;
+        gap: 10px;
+      }
+
+      header nav a {
+        position: relative;
+        text-decoration: none;
+        padding: 12px 18px;
+        color: #fff;
+        font-weight: 500;
+      }
+
+      header nav a.active {
+        background-color: #001540;
+        position: relative;
+        border-radius: 12px;
+      }
+    </style>
+
+    <link
+      rel="stylesheet"
+      href="https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200"
+    />
+  </head>
+  <body>
+    <main>
+      <header>
+        <a href="#" class="logo">
+          <img
+            src="https://aignosi.blob.core.windows.net/sientia/20231016-Aignosi_Logo_WHITE.png"
+            alt="Aignosi Logo"
+            width="247"
+            height="70"
+          />
+        </a>
+        <h1>Report</h1>
+        <nav>
+          <a href="#data_quality" class="active"> Summary </a>
+          <a href="#data_drift"> Drift </a>
+          <a href="#regression"> Regression </a>
+        </nav>
+        <div class="material-symbols-outlined">
+          info
+          <p class="tooltiptext">
+            Note that "current" <br />
+            is related to the test <br />
+            set while "reference" <br />
+            refers to the training <br />
+            set
+          </p>
+        </div>
+      </header>
+      <div class="quality_div">
+        <section id="data_quality"></section>
+      </div>
+      <div class="data_drift_div">
+        <section id="data_drift"></section>
+      </div>
+      <div class="regression_div">
+        <section id="regression"></section>
+      </div>
+    </main>
+
+    <script>
+      let sec = document.querySelectorAll("section");
+      let links = document.querySelectorAll("nav  a");
+
+      window.onscroll = () => {
+        sec.forEach((section) => {
+          let top = window.scrollY;
+          let offset = section.offsetTop;
+          let height = section.offsetHeight;
+          let id = section.getAttribute("id");
+
+          if (top >= offset && top < offset + height) {
+            links.forEach((link) => {
+              link.classList.remove("active");
+              document.querySelector("nav a[href*=" + id + "]").classList.add("active");
+            });
+          }
+        });
+      };
+    </script>
+  </body>
+</html>

model_manager/runtime_paths.py

@@ -0,0 +1,29 @@
+"""Filesystem layout for worker runtime data outside the application package tree."""
+
+from os import makedirs
+from os.path import join
+
+# Root for all mutable runtime data (not under /app; avoids clashing with git clone under /app).
+RUNTIME_DATA_ROOT = '/var/lib/model-manager'
+
+# Training reports (HTML, CSV exports, etc.) and related outputs.
+REPORTS_ROOT = join(RUNTIME_DATA_ROOT, 'reports')
+
+# project base path
+PROJECT_BASE_PATH = '/app/model_manager'
+
+# Per-training run folders (name + timestamp); cleanup cron deletes stale entries here.
+REPORTS_TEMP_DIR = join(REPORTS_ROOT, 'temp')
+
+# Worker log files when file logging is wired; stdout remains primary until then.
+LOGS_DIR = join(RUNTIME_DATA_ROOT, 'logs')
+
+
+def ensure_runtime_directories() -> None:
+    """Create runtime directories expected by the worker process."""
+    # REPORTS_ROOT: base directory for report artifacts; remove if all outputs move elsewhere.
+    makedirs(REPORTS_ROOT, exist_ok=True)
+    # REPORTS_TEMP_DIR: transient run subdirs; remove after retention/cleanup is centralized.
+    makedirs(REPORTS_TEMP_DIR, exist_ok=True)
+    # LOGS_DIR: on-disk logs; remove if logging stays stdout-only forever.
+    makedirs(LOGS_DIR, exist_ok=True)

model_manager/schedules/cleanup_schedule.py

@@ -0,0 +1,160 @@
+"""Schedule configuration for cleanup workflow."""
+
+import os
+from datetime import timedelta
+from typing import Any
+
+from sientia_do.observability.logger import Logger as SientiaLogger
+from temporalio.client import (
+    Client,
+    Schedule,
+    ScheduleActionStartWorkflow,
+    ScheduleSpec,
+)
+
+from model_manager.worker.prepare_worker import build_queue_name
+
+# Schedule configuration from environment variables
+CLEANUP_CRON = os.getenv('CLEANUP_CRON', '0 0 * * *')  # Default: midnight UTC
+CLEANUP_TIMEZONE = os.getenv('CLEANUP_TIMEZONE', 'UTC')
+CLEANUP_EXECUTION_TIMEOUT_HOURS = int(os.getenv('CLEANUP_EXECUTION_TIMEOUT_HOURS', '1'))
+
+
+def build_cleanup_schedule_id(runtime: str | None) -> str:
+    """
+    Build cleanup schedule ID using runtime-derived naming.
+
+    Args:
+        runtime: Runtime suffix used by workers
+
+    Return:
+        str: Cleanup schedule ID
+    """
+    normalized_runtime = runtime.strip() if runtime else ''
+    return f'cleanup-files-{normalized_runtime or "single"}-daily'
+
+
+async def _needs_schedule_reconcile(
+    schedule_handle: Any,
+    cleanup_task_queue: str,
+    logger: SientiaLogger,
+    metadata: dict[str, str | None],
+) -> bool:
+    """
+    Compare configured cleanup schedule against current expected values.
+
+    Args:
+        schedule_handle: Temporal schedule handle for current schedule ID
+        cleanup_task_queue: Expected cleanup task queue
+        logger: Logger instance for errors
+        metadata: Metadata dictionary for logging context
+
+    Return:
+        bool: True when schedule should be recreated to apply current config
+    """
+    try:
+        schedule_description = await schedule_handle.describe()
+        schedule = getattr(schedule_description, 'schedule', None)
+        action = getattr(schedule, 'action', None)
+        spec = getattr(schedule, 'spec', None)
+
+        current_task_queue = getattr(action, 'task_queue', None)
+        current_execution_timeout = getattr(action, 'execution_timeout', None)
+        current_cron = getattr(spec, 'cron_expressions', None)
+        current_timezone = getattr(spec, 'time_zone_name', None)
+
+        return (
+            current_task_queue != cleanup_task_queue
+            or current_execution_timeout != timedelta(hours=CLEANUP_EXECUTION_TIMEOUT_HOURS)
+            or current_cron != [CLEANUP_CRON]
+            or current_timezone != CLEANUP_TIMEZONE
+        )
+    except Exception as e:  # noqa: BLE001
+        logger.custom_error(f'Error describing cleanup schedule for reconcile: {e}', metadata)
+        return True
+
+
+async def schedule_exists(
+    client: Client, schedule_id: str, logger: SientiaLogger, metadata: dict[str, str | None]
+) -> bool:
+    """
+    Check if a schedule already exists.
+
+    Args:
+        client: Temporal client instance
+        schedule_id: ID of the schedule to check
+        logger: Logger instance for error logging
+        metadata: Metadata dictionary for logging context
+
+    Returns:
+        True if schedule exists, False otherwise
+    """
+    try:
+        async for schedule in await client.list_schedules():
+            if schedule.id == schedule_id:
+                return True
+        return False
+    except Exception as e:  # noqa: BLE001
+        logger.custom_error(f'Error checking if schedule exists: {e}', metadata)
+        return False
+
+
+async def create_cleanup_schedule(
+    client: Client, logger: SientiaLogger, metadata: dict[str, str | None]
+) -> None:
+    """
+    Create or update the cleanup files schedule.
+
+    This function is idempotent and can be called multiple times safely.
+    It will only create the schedule if it doesn't already exist.
+
+    Args:
+        client: Temporal client instance
+        logger: Logger instance for logging schedule operations
+        metadata: Metadata dictionary for logging context
+    """
+    runtime = (os.getenv('RUNTIME') or 'single').strip()
+    cleanup_task_queue = build_queue_name('CleanupFiles', runtime or 'single')
+    schedule_id = build_cleanup_schedule_id(runtime)
+    updated = False
+
+    if await schedule_exists(client, schedule_id, logger, metadata):
+        handle = client.get_schedule_handle(schedule_id)
+        if await _needs_schedule_reconcile(handle, cleanup_task_queue, logger, metadata):
+            await handle.delete()
+            updated = True
+        else:
+            logger.custom_info(
+                f"Schedule '{schedule_id}' is already up to date, no-op reconcile",
+                metadata,
+            )
+            return
+
+    await client.create_schedule(
+        schedule_id,
+        Schedule(
+            action=ScheduleActionStartWorkflow(
+                'cleanup_files',
+                {},  # Empty input, will use default bucket from environment
+                id=f'cleanup-files-scheduled-{schedule_id}',
+                task_queue=cleanup_task_queue,
+                execution_timeout=timedelta(hours=CLEANUP_EXECUTION_TIMEOUT_HOURS),
+            ),
+            spec=ScheduleSpec(
+                cron_expressions=[CLEANUP_CRON],
+                time_zone_name=CLEANUP_TIMEZONE,
+            ),
+        ),
+    )
+    if updated:
+        logger.custom_info(
+            f"Schedule '{schedule_id}' reconciled successfully. "
+            f'Cleanup will run at: {CLEANUP_CRON} ({CLEANUP_TIMEZONE})',
+            metadata,
+        )
+    else:
+        logger.custom_info(
+            f"Schedule '{schedule_id}' created successfully. "
+            f'Cleanup will run at: {CLEANUP_CRON} ({CLEANUP_TIMEZONE})',
+            metadata,
+        )

model_manager/sientia/exceptions.py

@@ -0,0 +1,3 @@
+from mlflow.exceptions import MlflowException
+
+SientiaMlException = MlflowException

model_manager/sientia/metrics.py

@@ -0,0 +1,148 @@
+import numpy as np
+import pandas as pd
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+
+def mse(real_data: pd.Series, predictions: pd.Series) -> float:
+    """
+    Calculates the mean squared error between the real data and the predictions.
+    """
+    return round(
+        mean_squared_error(real_data.astype(np.float64), predictions.astype(np.float64)), 2
+    )
+
+
+def mae(real_data: pd.Series, predictions: pd.Series) -> float:
+    """
+    Calculates the mean absolute error between the real data and the predictions.
+    """
+    return round(
+        mean_absolute_error(real_data.astype(np.float64), predictions.astype(np.float64)), 2
+    )
+
+
+def r2(real_data: pd.Series, predictions: pd.Series) -> float:
+    """
+    Calculates the R2 score between the real data and the predictions.
+    """
+    return round(r2_score(real_data.astype(np.float64), predictions.astype(np.float64)), 2)
+
+
+def silverman_radius(data: np.ndarray) -> float:
+    """
+    Calculate the Silverman bandwidth (radius) for a given dataset.
+
+    Args:
+        data (np.ndarray): Input data (1D array)
+
+    Returns:
+        float: Silverman bandwidth (radius)
+    """
+    n = len(data)
+    sigma = np.std(data)
+    iqr = np.percentile(data, 75) - np.percentile(data, 25)
+    radius = 0.9 * min(sigma, iqr / 1.34) * n ** (-1 / 5)
+    return radius
+
+
+def rce_train(training_set: pd.DataFrame, radius: float | None = None) -> pd.DataFrame:
+    """
+    Get the Reduced Coulomb Energy (RCE) prototypes.
+
+    Args:
+        training_set (pd.DataFrame): The training set
+        radius (float | None): The radius of the RCE prototypes. If None, computed using Silverman's rule.
+
+    Returns:
+        pd.DataFrame: The RCE prototypes
+    """
+    train_vectors = training_set.values
+
+    # Vectorized distance computation for the radius calculation
+    diff_vectors = train_vectors[:, np.newaxis] - train_vectors[np.newaxis, :]
+    distances = np.linalg.norm(diff_vectors, axis=-1)
+
+    # Non-parametric radius: Silverman Radius (compute if not provided)
+    effective_radius = radius if radius is not None else silverman_radius(distances.flatten())
+
+    # Initialize prototypes with the first vector
+    prototypes = [train_vectors[0]]
+
+    for vector in train_vectors[1:]:
+        # Vectorized distance check between current vector and all prototypes
+        distances_to_prototypes = np.linalg.norm(prototypes - vector, axis=1)
+
+        # If no prototype is close, add the current vector as a new prototype
+        if np.all(distances_to_prototypes > effective_radius):
+            prototypes.append(vector)
+
+    return pd.DataFrame(prototypes)
+
+
+def rce_test(test_set: pd.DataFrame, prototypes: pd.DataFrame) -> pd.Series:
+    """
+    Get the signed Reduced Coulomb Energy (RCE) predictions.
+
+    Args:
+        test_set (pd.DataFrame): The test set
+        prototypes (pd.DataFrame): The RCE prototypes
+
+    Returns:
+        pd.Series: The signed distances to the closest prototype for each test vector
+    """
+    test_vectors = test_set.values
+    prototype_vectors = prototypes.values
+
+    # Vectorized computation of distances between test vectors and all prototypes
+    diff_vectors = test_vectors[:, np.newaxis] - prototype_vectors[np.newaxis, :]
+    distances = np.linalg.norm(diff_vectors, axis=-1)
+
+    # Find the closest prototype for each test vector
+    min_distances = np.min(distances, axis=1)
+    closest_prototypes = prototype_vectors[np.argmin(distances, axis=1)]
+
+    # Compute the signed distance for each test vector
+    signed_distances = np.sqrt(min_distances**2) * np.sign(
+        np.mean(test_vectors - closest_prototypes, axis=1)
+    )
+
+    return pd.Series(signed_distances)
+
+
+def rce_drift(reference_data: pd.DataFrame, real_data: pd.DataFrame, column: str) -> pd.Series:
+    """
+    Detect drift using the Reduced Coulomb Energy (RCE) method.
+
+    Args:
+        reference_data (pd.DataFrame): The reference data
+        real_data (pd.DataFrame): The real data
+        column (str): The target column to be analyzed. 'target' or 'prediction'
+
+    Returns:
+        pd.Series: Normalized drift distances
+    """
+    common_columns = list(set(reference_data.columns).intersection(real_data.columns))
+    reference_data = reference_data[common_columns]
+    real_data = real_data[common_columns]
+
+    # Get prototypes
+    if column == 'target':
+        prototypes = rce_train(reference_data.drop(columns=['prediction']), 0.1)
+    else:
+        prototypes = rce_train(reference_data.drop(columns=['target']), 0.1)
+
+    # Distances to prototypes
+    if column == 'target':
+        distances_train = rce_test(reference_data.drop(columns=['prediction']), prototypes)
+        distances_test = rce_test(real_data.drop(columns=['prediction']), prototypes)
+    else:
+        distances_train = rce_test(reference_data.drop(columns=['target']), prototypes)
+        distances_test = rce_test(real_data.drop(columns=['target']), prototypes)
+
+    # Find the maximum absolute distance in the training set
+    max_abs_distance = max(abs(distances_train.max()), abs(distances_train.min()))
+
+    # Normalize while preserving sign
+    distances = distances_test / max_abs_distance
+
+    return distances

model_manager/sientia/reports.py

@@ -0,0 +1,292 @@
+import os
+from collections.abc import Sequence
+from typing import Any
+
+from bs4 import BeautifulSoup, Tag
+from evidently.metric_preset import DataDriftPreset
+from evidently.metrics import (
+    ColumnSummaryMetric,
+    ConflictTargetMetric,
+    DatasetCorrelationsMetric,
+    DatasetSummaryMetric,
+    RegressionAbsPercentageErrorPlot,
+    RegressionDummyMetric,
+    RegressionErrorDistribution,
+    RegressionErrorPlot,
+    RegressionPerformanceMetrics,
+    RegressionPredictedVsActualPlot,
+    RegressionPredictedVsActualScatter,
+)
+from evidently.metrics.base_metric import generate_column_metrics
+from evidently.options import ColorOptions
+from evidently.pipeline.column_mapping import ColumnMapping
+from evidently.report import Report
+
+COLOR_DISCRETE_SEQUENCE = (
+    '#ed0400',
+    '#0a5f38',
+    '#6c3461',
+    '#71aa34',
+    '#d8dcd6',
+    '#6b8ba4',
+)
+
+
+def load_html_from_file(file_path):
+    with open(file_path, encoding='utf-8') as file:
+        return file.read()
+
+
+def inject_content(main_html, section_id, content):
+    soup = BeautifulSoup(main_html, 'html.parser')
+    section = soup.find(id=section_id)
+
+    # Verifica se a seção foi encontrada E se ela é uma Tag (não uma string)
+    if section and isinstance(section, Tag):
+        section.clear()
+        # Converte o conteúdo para um fragmento de BeautifulSoup e anexa
+        new_content = BeautifulSoup(content, 'html.parser')
+        section.append(new_content)
+
+    return str(soup)
+
+
+class Reports:
+    """
+    Report generator using Evidently library.
+
+    Thread-safety: This class is NOT thread-safe. Multiple threads should not
+    call add_*_section() methods on the same instance simultaneously as they
+    modify shared state (self.metrics, self.sections, self.options).
+
+    For multi-threaded environments:
+    - Create separate Reports instances per thread
+    - Or synchronize access using locks
+    - After generation, instances are safe for read-only operations
+
+    I/O Note: This class relies on Evidently's report.save_html() method
+    for file operations. Ensure Evidently properly manages file handles.
+    """
+
+    def __init__(
+        self,
+        reference_data: Any,
+        current_data: Any,
+        target_name: str,
+        base_path: str | None = None,
+        template_path: str | None = None,
+    ) -> None:
+        """
+        Initializes an instance of the AigReport class.
+
+        Args:
+            reference_data: The reference data for the report.
+            current_data: The current data for the report.
+            base_path: The base path for the report.
+        """
+        self.metrics: list[Any] = []
+        self.options: list[Any] | None = None
+        self.sections: dict[str, Any] = {}
+        self.report: Any = None
+        self.ref_data = reference_data
+        self.cur_data = current_data
+        self.target_name = target_name
+        self.set_color_options(primary_color='#0F4C81', secondary_color='#001E60')
+        self.base_path = base_path
+        self.template_path = template_path
+
+    def add_data_quality_section(self, columns: list[str] | None = None, run: bool = True) -> None:
+        """
+        Adds a data quality section to the report.
+
+        Args:
+            columns: The list of columns to include in the data quality section. If None, all columns will be included.
+            run: Indicates whether to run the report immediately after adding the section.
+        """
+        metrics = [
+            DatasetSummaryMetric(),
+            generate_column_metrics(ColumnSummaryMetric, columns=columns, skip_id_column=True),
+            ConflictTargetMetric(),
+            DatasetCorrelationsMetric(),
+        ]
+        self.metrics.extend(metrics)
+        if run:
+            mapping = ColumnMapping()
+            mapping.target = self.target_name
+            report = Report(metrics=metrics, options=self.options)
+            report.run(
+                reference_data=self.ref_data,
+                current_data=self.cur_data,
+                column_mapping=mapping,
+            )
+            self.sections['data_quality'] = report.as_dict()
+            if self.base_path:
+                # Note: Relies on Evidently's save_html() to properly manage file I/O
+                report.save_html(os.path.join(self.base_path, 'data_quality.html'))
+
+    def add_data_drift_section(self, columns: list[str] | None = None, run: bool = True) -> None:
+        """
+        Adds a data drift section to the report.
+
+        Args:
+            columns: The list of columns to include in the data drift section. If None, all columns will be included.
+            run: Indicates whether to run the report immediately after adding the section.
+        """
+        self.metrics.append(DataDriftPreset(columns=columns))
+        if run:
+            mapping = ColumnMapping()
+            mapping.target = self.target_name
+            report = Report(metrics=[DataDriftPreset(columns=columns)], options=self.options)
+            report.run(
+                reference_data=self.ref_data,
+                current_data=self.cur_data,
+                column_mapping=mapping,
+            )
+            self.sections['data_drift'] = report.as_dict()
+            if self.base_path:
+                # Note: Relies on Evidently's save_html() to properly manage file I/O
+                report.save_html(os.path.join(self.base_path, 'data_drift.html'))
+
+    def add_regression_section(self, run: bool = True) -> None:
+        """
+        Adds a regression section to the report.
+
+        Args:
+            run: Indicates whether to run the report immediately after adding the section.
+        """
+        metrics = [
+            RegressionPerformanceMetrics(),
+            RegressionDummyMetric(),
+            RegressionPredictedVsActualScatter(),
+            RegressionPredictedVsActualPlot(),
+            RegressionErrorPlot(),
+            RegressionAbsPercentageErrorPlot(),
+            RegressionErrorDistribution(),
+        ]
+        self.metrics.extend(metrics)
+        if run:
+            mapping = ColumnMapping()
+
+            mapping.target = self.target_name
+            mapping.prediction = 'prediction'
+
+            report = Report(metrics=metrics, options=self.options)
+            report.run(
+                reference_data=self.ref_data,
+                current_data=self.cur_data,
+                column_mapping=mapping,
+            )
+            self.sections['regression'] = report.as_dict()
+            if self.base_path:
+                # Note: Relies on Evidently's save_html() to properly manage file I/O
+                report.save_html(os.path.join(self.base_path, 'regression.html'))
+
+    def set_color_options(
+        self,
+        primary_color: str = '#0F4C81',
+        secondary_color: str = '#001E60',
+        current_data_color: str | None = None,
+        reference_data_color: str | None = None,
+        additional_data_color: str = '#0a5f38',
+        color_sequence: Sequence[str] = COLOR_DISCRETE_SEQUENCE,
+        fill_color: str = 'LightGreen',
+        zero_line_color: str = 'green',
+        non_visible_color: str = 'white',
+        underestimation_color: str = '#6574f7',
+        overestimation_color: str = '#ee5540',
+        majority_color: str = '#1acc98',
+        vertical_lines: str = 'green',
+        heatmap: str = 'RdBu_r',
+    ) -> None:
+        """
+        Sets the color options for the report.
+
+        Args:
+            primary_color: The primary color for the report.
+            secondary_color: The secondary color for the report.
+            current_data_color: The color for the current data.
+            reference_data_color: The color for the reference data.
+            additional_data_color: The color for additional data.
+            color_sequence: The color sequence for discrete values.
+            fill_color: The fill color for visualizations.
+            zero_line_color: The color for the zero line.
+            non_visible_color: The color for non-visible elements.
+            underestimation_color: The color for underestimation.
+            overestimation_color: The color for overestimation.
+            majority_color: The color for majority elements.
+            vertical_lines: The color for vertical lines.
+            heatmap: The color map for heatmaps.
+        """
+        color_scheme = ColorOptions(
+            primary_color=primary_color,
+            secondary_color=secondary_color,
+            current_data_color=current_data_color,
+            reference_data_color=reference_data_color,
+            additional_data_color=additional_data_color,
+            color_sequence=color_sequence,
+            fill_color=fill_color,
+            zero_line_color=zero_line_color,
+            non_visible_color=non_visible_color,
+            underestimation_color=underestimation_color,
+            overestimation_color=overestimation_color,
+            majority_color=majority_color,
+            vertical_lines=vertical_lines,
+            heatmap=heatmap,
+        )
+
+        if self.options is None:
+            self.options = [color_scheme]
+        else:
+            self.options.append(color_scheme)
+
+    def save_all_sections_html(self, report_path):
+        """
+        Saves the report with all sections as HTML.
+
+        Args:
+            report_path: The path to save the report HTML file.
+
+        Raises:
+            ValueError: If base_path is not set
+            OSError: If directory creation or file writing fails
+
+        Note:
+            This method uses context manager (with open) to ensure file is properly closed.
+            Creates parent directories if they don't exist.
+        """
+        if not self.base_path:
+            raise ValueError('base_path is required to save all sections HTML')
+
+        if not self.template_path:
+            raise ValueError('template_path is required to save all sections HTML')
+
+        # Ensure output directory exists
+        output_dir = os.path.dirname(report_path)
+        if output_dir and not os.path.exists(output_dir):
+            os.makedirs(output_dir, exist_ok=True)
+
+        print(f'Output directory: {output_dir}')
+        print(f'Report path: {report_path}')
+        print(f'Base path: {self.base_path}')
+        print(f'Template path: {self.template_path}')
+
+        # Load main HTML template
+        main_html_path = os.path.join(self.template_path, 'header.html')
+        main_html = load_html_from_file(main_html_path)
+
+        # Load content from data_drift.html, data_quality.html, and regression.html
+        data_drift_content = load_html_from_file(os.path.join(self.base_path, 'data_drift.html'))
+        data_quality_content = load_html_from_file(
+            os.path.join(self.base_path, 'data_quality.html')
+        )
+        regression_content = load_html_from_file(os.path.join(self.base_path, 'regression.html'))
+
+        # Inject content into the main HTML template
+        main_html = inject_content(main_html, 'data_drift', data_drift_content)
+        main_html = inject_content(main_html, 'data_quality', data_quality_content)
+        main_html = inject_content(main_html, 'regression', regression_content)
+
+        # Save the final HTML to a new file (report.html)
+        # Context manager ensures file is properly closed even if an error occurs
+        with open(report_path, 'w', encoding='utf-8') as report_file:
+            report_file.write(main_html)

model_manager/utils/connectors_config.py

@@ -0,0 +1,166 @@
+from os import getenv
+from typing import Any
+
+
+def build_postgres_config() -> dict[str, Any]:
+    """
+    Build PostgreSQL database configuration from environment variables.
+
+    This function constructs a PostgreSQL configuration dictionary from
+    environment variables with sensible defaults for local development.
+    It handles connection pool configuration and security parameters.
+
+    Environment Variables:
+        POSTGRES_HOST: Database hostname (default: localhost)
+        POSTGRES_PORT: Database port (default: 5432)
+        POSTGRES_USER: Database username (default: sientia)
+        POSTGRES_PASSWORD: Database password (default: sientia)
+        POSTGRES_DBNAME: Database name (default: sientia)
+        POSTGRES_MIN_CONNECTIONS: Minimum connection pool size (default: 5)
+        POSTGRES_MAX_CONNECTIONS: Maximum connection pool size (default: 20)
+
+    Returns:
+        dict: PostgreSQL configuration dictionary with all required parameters
+    """
+    return {
+        'host': getenv('POSTGRES_HOST', 'localhost'),
+        'port': int(getenv('POSTGRES_PORT', '5432')),
+        'user': getenv('POSTGRES_USER', 'sientia'),
+        'password': getenv('POSTGRES_PASSWORD', 'sientia'),
+        'dbname': getenv('POSTGRES_DBNAME', 'sientia'),
+        'min_connections': int(getenv('POSTGRES_MIN_CONNECTIONS', '5')),
+        'max_connections': int(getenv('POSTGRES_MAX_CONNECTIONS', '20')),
+    }
+
+
+def build_mlflow_config() -> dict[str, Any]:
+    """
+    Build MLFlow server configuration from environment variables.
+
+    This function constructs an MLFlow configuration dictionary from
+    environment variables with sensible defaults for local development.
+    It handles server connection and authentication parameters.
+
+    Environment Variables:
+        MLFLOW_URL: Full MLflow tracking URL including scheme, host, and port
+                    (default: http://localhost:5080)
+        MLFLOW_USERNAME: MLFlow username (default: aignosi)
+        MLFLOW_PASSWORD: MLFlow password (default: aignosi)
+
+    Returns:
+        dict: MLFlow configuration dictionary with all required parameters
+    """
+    return {
+        'url': getenv('MLFLOW_URL', 'http://localhost:5080'),
+        'username': getenv('MLFLOW_USERNAME', 'aignosi'),
+        'password': getenv('MLFLOW_PASSWORD', 'aignosi'),
+    }
+
+
+def build_mongodb_config() -> dict[str, Any]:
+    """
+    Build MongoDB configuration from environment variables.
+
+    This function constructs a MongoDB configuration dictionary from
+    environment variables with sensible defaults for local development.
+    It handles connection string and database name configuration.
+
+    Environment Variables:
+        MONGODB_USERNAME: MongoDB username (default: root)
+        MONGODB_PASSWORD: MongoDB password (default: wKZDbMNU1c)
+        MONGODB_URL: MongoDB connection URI (default: localhost:27018)
+        MONGODB_DATABASE: MongoDB database name (default: sientia)
+        MONGODB_TTL_INDEX_HOURS: TTL index duration in hours (default: 1)
+
+    Returns:
+        dict: MongoDB configuration dictionary with connection parameters
+    """
+    username = getenv('MONGODB_USERNAME', 'root')
+    password = getenv('MONGODB_PASSWORD', 'wKZDbMNU1c')
+    uri = getenv('MONGODB_URL', 'localhost:27018')
+
+    connection_string = f'mongodb://{username}:{password}@{uri}'
+
+    return {
+        'connection_string': connection_string,
+        'database_name': getenv('MONGODB_DATABASE', 'sientia'),
+        'ttl_index_seconds': int(getenv('MONGODB_TTL_INDEX_HOURS', '1')) * 3600,
+        'uri': uri,
+    }
+
+
+def build_minio_config() -> dict[str, Any]:
+    """
+    Build MinIO (S3-compatible) configuration from environment variables.
+
+    This function constructs a MinIO configuration dictionary from
+    environment variables with sensible defaults for local development.
+    It handles endpoint URL, authentication, connection parameters, and retry policies.
+
+    Environment Variables:
+        MINIO_ENDPOINT_URL: MinIO server endpoint URL (default: http://localhost:9000)
+        MINIO_ACCESS_KEY: MinIO access key ID (default: minioadmin)
+        MINIO_SECRET_KEY: MinIO secret access key (default: minioadmin)
+        MINIO_REGION: MinIO region name (default: us-east-1)
+        MINIO_SECURE: Whether to use SSL/TLS (default: false)
+        MINIO_MAX_RETRY_ATTEMPTS: Maximum number of retry attempts (default: 3)
+        MINIO_RETRY_MODE: Retry mode - standard, legacy, or adaptive (default: adaptive)
+        MINIO_CONNECT_TIMEOUT: Connection timeout in seconds (default: 10)
+        MINIO_READ_TIMEOUT: Read timeout in seconds (default: 60)
+        MINIO_DEFAULT_BUCKET: Default S3 bucket for MinioRepository (default: model-training)
+
+    Returns:
+        dict: MinIO configuration dictionary with all required parameters
+    """
+    return {
+        'endpoint_url': getenv('MINIO_ENDPOINT_URL', 'http://localhost:9000'),
+        'access_key': getenv('MINIO_ACCESS_KEY', 'minioadmin'),
+        'secret_key': getenv('MINIO_SECRET_KEY', 'minioadmin'),
+        'region': getenv('MINIO_REGION', 'us-east-1'),
+        'use_ssl': getenv('MINIO_SECURE', 'false').lower() == 'true',
+        'max_retry_attempts': int(getenv('MINIO_MAX_RETRY_ATTEMPTS', '3')),
+        'retry_mode': getenv('MINIO_RETRY_MODE', 'adaptive'),
+        'connect_timeout': int(getenv('MINIO_CONNECT_TIMEOUT', '10')),
+        'read_timeout': int(getenv('MINIO_READ_TIMEOUT', '60')),
+        'default_bucket': getenv('MINIO_DEFAULT_BUCKET', 'model-training'),
+    }
+
+
+def build_plugin_store_config() -> dict[str, Any]:
+    """
+    Build PluginStore configuration from environment variables.
+
+    This function constructs a configuration dictionary for the PluginStore
+    client using environment variables with sensible defaults for local
+    development.
+
+    Environment Variables:
+        STORE_BASE_URL: Base URL of the PluginStore backing Git server
+                         (default: http://localhost:3000)
+        STORE_OWNER: Repository owner/organization (default: sientia)
+        STORE_REPO: Repository name (default: model-library-store)
+        STORE_BRANCH: Optional branch name
+        STORE_USERNAME: Optional username for Git HTTP authentication
+        STORE_PASSWORD: Optional password/token for Git HTTP authentication
+        PYPI_SERVER: Optional custom PyPI index URL for runtime installation
+                     (default: http://localhost:5000)
+        PYPI_USERNAME: Optional username for PyPI authentication
+        PYPI_PASSWORD: Optional password/token for PyPI authentication
+
+    Returns:
+        dict: PluginStore configuration dictionary with all connector parameters
+    """
+
+    cache_ttl_seconds = getenv('STORE_CACHE_TTL_SECONDS')
+    return {
+        'base_url': getenv('STORE_BASE_URL', 'http://localhost:3000'),
+        'owner': getenv('STORE_OWNER', 'sientia'),
+        'repo': getenv('STORE_REPO', 'model-library-store'),
+        'username': getenv('STORE_USERNAME'),
+        'password': getenv('STORE_PASSWORD'),
+        'branch': getenv('STORE_BRANCH'),
+        'cache_ttl_seconds': int(cache_ttl_seconds) if cache_ttl_seconds else None,
+        'pypi_index_url': getenv('PYPI_SERVER', 'http://localhost:5000'),
+        'pypi_username': getenv('PYPI_USERNAME'),
+        'pypi_password': getenv('PYPI_PASSWORD'),
+    }

model_manager/utils/logger_helper.py

@@ -0,0 +1,27 @@
+"""
+Logger helper to prevent duplicate logs caused by propagation.
+
+This module provides a wrapper around sientia_do Logger to disable
+log propagation and prevent duplicate log entries in the Model Manager.
+"""
+
+from sientia_do.observability.logger import Logger as SientiaLogger
+
+
+def get_logger(name: str) -> SientiaLogger:
+    """
+    Create a Logger instance with propagation disabled.
+
+    This prevents duplicate logs caused by hierarchical propagation
+    in Python's logging system.
+
+    Args:
+        name: Logger name (typically __name__ of the calling module).
+
+    Returns:
+        Logger: Configured logger instance with propagation disabled.
+    """
+    logger = SientiaLogger(name)
+    # Disable propagation to prevent duplicate logs
+    logger.base_logger.propagate = False
+    return logger

model_manager/utils/models/__init__.py

@@ -0,0 +1,16 @@
+"""
+Models and DTOs for the Model Manager system.
+
+This module contains data transfer objects (DTOs) and model classes used
+throughout the Model Manager workflows and activities.
+"""
+
+from model_manager.utils.models.experiment_status import ExperimentStatus
+from model_manager.utils.models.train_model_params import TrainModelParams
+from model_manager.utils.models.train_model_result import TrainModelResult
+
+__all__ = [
+    'ExperimentStatus',
+    'TrainModelParams',
+    'TrainModelResult',
+]

model_manager/utils/models/experiment_status.py

@@ -0,0 +1,26 @@
+from enum import StrEnum
+
+
+class ExperimentStatus(StrEnum):
+    """
+    Status values for experiment run lifecycle.
+
+    This enum defines all possible status values that an experiment run can have
+    throughout its lifecycle, from initialization through training, model saving,
+    and cleanup. These statuses are used to track progress and identify failures
+    in the training pipeline.
+
+    The status values follow the naming convention from the original Mage pipeline
+    to maintain compatibility with existing database records and monitoring systems.
+
+    Attributes:
+        ORCHESTRATOR_WAITING_PROC: Initial status indicating experiment is registered and waiting for processing.
+        ORCHESTRATOR_VALIDATION_ERROR: Error in the parameters validation.
+        TRAINING_SUCCESS: Training completed successfully with model and metrics calculated.
+        TRAINING_ERROR: Training failed due to data issues, model errors, or other exceptions.
+    """
+
+    ORCHESTRATOR_VALIDATION_ERROR = 'ORCHESTRATOR_VALIDATION_ERROR'
+    ORCHESTRATOR_WAITING_PROC = 'ORCHESTRATOR_WAITING_PROC'
+    TRAINING_SUCCESS = 'TRAINING_SUCCESS'
+    TRAINING_ERROR = 'TRAINING_ERROR'

model_manager/utils/models/train_model_params.py

@@ -0,0 +1,363 @@
+from dataclasses import dataclass
+from typing import Any
+
+from jsonschema import Draft202012Validator, ValidationError  # type: ignore[import-untyped]
+
+# Allowed frontend date formats and their strftime equivalents (single source of truth)
+FRONTEND_DATE_FORMAT_TO_STRFTIME = {
+    'dd/MM/yyyy HH:mm:ss': '%d/%m/%Y %H:%M:%S',
+    'MM/dd/yyyy HH:mm:ss': '%m/%d/%Y %H:%M:%S',
+    'yyyy/MM/dd HH:mm:ss': '%Y/%m/%d %H:%M:%S',
+    'dd-MM-yyyy HH:mm:ss': '%d-%m-%Y %H:%M:%S',
+    'MM-dd-yyyy HH:mm:ss': '%m-%d-%Y %H:%M:%S',
+    'yyyy-MM-dd HH:mm:ss': '%Y-%m-%d %H:%M:%S',
+}
+ALLOWED_FRONTEND_DATE_FORMATS = frozenset(FRONTEND_DATE_FORMAT_TO_STRFTIME.keys())
+
+# When the client omits date_format (or sends null/blank), parsing uses this frontend format.
+DEFAULT_TRAIN_DATE_FORMAT = 'yyyy-MM-dd HH:mm:ss'
+
+
+def validate_frontend_date_format(fmt: str | None) -> None:
+    """Raise ValueError if fmt is set and not one of the allowed frontend date formats."""
+    if not fmt or not fmt.strip():
+        return
+    if fmt not in ALLOWED_FRONTEND_DATE_FORMATS:
+        allowed = ', '.join(sorted(ALLOWED_FRONTEND_DATE_FORMATS))
+        raise ValueError(f'Invalid date_format "{fmt}". Allowed formats: {allowed}')
+
+
+# Model name constants
+MODEL_LINEAR_REGRESSION = 'Linear Regression'
+MODEL_POLYNOMIAL_REGRESSION = 'Polynomial Regression'
+
+
+@dataclass
+class TrainModelParams:
+    """
+    Parameters for machine learning model training.
+
+    This class encapsulates all configuration parameters required for the training
+    pipeline, including data processing settings, model configuration, and experiment
+    tracking information. All parameters are validated upon initialization to ensure
+    data integrity and prevent runtime errors.
+
+    Use the `from_dict()` class method to create instances from dictionaries with
+    automatic validation of all fields.
+
+    Attributes:
+        variable_columns (list[str]): List of variable column names to use as features.
+        target_variable (str): Name of the target variable to predict.
+        bucket_name (str): Name of the MinIO bucket containing training data.
+        file_name (str): Name of the file in the MinIO bucket.
+        line_separator (str): Line separator used in the CSV file.
+        decimal_separator (str): Decimal separator used in the CSV file.
+        date_column (str): Name of the date/time column in the dataset (required).
+        date_format (str): Format of the date column (allowed frontend strings). If omitted or blank
+            in the input dict, defaults to DEFAULT_TRAIN_DATE_FORMAT.
+        train_size (int): Percentage of data to use for training (0-100).
+        shuffle (bool): Whether to shuffle the data during train/test split.
+        experiment_run_id (int): Unique identifier for the experiment run.
+        model_name (str): Name of the model type ('Linear Regression' or 'Polynomial Regression').
+        val_file_name (str | None): Name of the validation file in the MinIO bucket.
+        data_model_kwargs (dict | None): Keyword arguments for the data model.
+        model_kwargs (dict | None): Keyword arguments for the model.
+        opt_params (dict | None): Keyword optimazation arguments for the wrapper.
+        model_type (str): Type of the model to use (ex.: 'Linear Regression', 'XGBoost').
+    """
+
+    # Old Parameters (keep)
+
+    variable_columns: list[str]
+    target_variable: str
+    bucket_name: str
+    file_name: str
+    line_separator: str
+    decimal_separator: str
+    date_column: str
+    date_format: str
+    train_size: int
+    shuffle: bool
+    random_state: int
+    experiment_run_id: int
+    model_name: str
+    experiment_name: str
+
+    # New Parameters
+    val_file_name: str | None
+    data_model_kwargs: dict | None  # Removed params used in DataPreprocessor here
+    model_kwargs: dict | None  # Removed params used in Linear Regression Model here
+    opt_params: dict | None
+    model_type: str
+    model_id: str | None
+
+    # Context Parameters
+    model_metadata: dict | None
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> 'TrainModelParams':
+        """
+        Create TrainModelParams from dictionary with validation.
+
+        This factory method creates a TrainModelParams instance from a dictionary,
+        applying validation to ensure all required fields are present and have
+        the correct types. This is the recommended way to create instances from
+        workflow input data.
+
+        Args:
+            data: Dictionary containing training parameters with keys matching the
+                  attribute names (e.g. variable_columns, date_column, data_model_kwargs, model_kwargs, opt_params).
+                  Unknown keys are ignored by from_dict; missing required snake_case keys raise.
+                  model_metadata may be omitted or None until load_model_metadata fills it.
+                  experiment_run_id may be an int or numeric string.
+
+        Returns:
+            TrainModelParams: Validated instance with all fields populated
+
+        Raises:
+            ValueError: If any required field is missing or None
+            TypeError: If any field has an incorrect type
+            KeyError: If any required key is missing from the dictionary
+        """
+        # `from_dict()` should only build the "raw" object from the input dict.
+        # Semantic validation and defaults must be handled by `validate_business_rules()`
+        # (using `model_metadata` JSON Schemas).
+
+        model_name = cls._check_none(data.get('model_name'), str, 'model_name')
+
+        return cls(
+            variable_columns=cls._check_none(
+                data.get('variable_columns'), list, 'variable_columns'
+            ),
+            target_variable=cls._check_none(data.get('target_variable'), str, 'target_variable'),
+            bucket_name=cls._check_none(data.get('bucket_name'), str, 'bucket_name'),
+            file_name=cls._check_none(data.get('file_name'), str, 'file_name'),
+            line_separator=cls._check_none(data.get('line_separator'), str, 'line_separator'),
+            decimal_separator=cls._check_none(
+                data.get('decimal_separator'), str, 'decimal_separator'
+            ),
+            date_column=cls._check_none(data.get('date_column'), str, 'date_column'),
+            date_format=cls._resolve_date_format(data.get('date_format')),
+            train_size=cls._check_none(data.get('train_size'), int, 'train_size'),
+            shuffle=cls._check_none(data.get('shuffle'), bool, 'shuffle'),
+            random_state=cls._check_none(data.get('random_state', 42), int, 'random_state'),
+            experiment_run_id=cls._coerce_experiment_run_id(data.get('experiment_run_id')),
+            model_name=model_name,
+            experiment_name=model_name,
+            val_file_name=data.get('val_file_name'),
+            data_model_kwargs=cls._check_none(
+                data.get('data_model_kwargs'), dict, 'data_model_kwargs'
+            ),
+            model_kwargs=cls._check_none(data.get('model_kwargs'), dict, 'model_kwargs'),
+            opt_params=cls._check_none(data.get('opt_params'), dict, 'opt_params'),
+            model_type=cls._check_none(data.get('model_type'), str, 'model_type'),
+            model_id=data.get('model_id'),
+            model_metadata=cls._parse_optional_model_metadata(data.get('model_metadata')),
+        )
+
+    @staticmethod
+    def _resolve_date_format(raw: Any) -> str:
+        """
+        Resolve date_format from workflow input.
+
+        Omitted, null, or blank values use DEFAULT_TRAIN_DATE_FORMAT. Non-string types raise.
+
+        Args:
+            raw: Raw date_format from the payload, or None if absent.
+
+        Return:
+            str: Canonical frontend date format string.
+        """
+        if raw is None:
+            return DEFAULT_TRAIN_DATE_FORMAT
+        if isinstance(raw, str) and not raw.strip():
+            return DEFAULT_TRAIN_DATE_FORMAT
+        if not isinstance(raw, str):
+            raise TypeError(
+                f'date_format must be a string or omitted, but got {type(raw).__name__}.'
+            )
+        return raw.strip()
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Convert TrainModelParams to a dictionary.
+        """
+        return self.__dict__
+
+    @staticmethod
+    def _check_none(value: Any | None, expected_type: type, field_name: str) -> Any:
+        """
+        Validate that a value is not None and check its type.
+
+        This method ensures that required parameters are provided and have the
+        correct type, raising descriptive errors if validation fails.
+
+        Args:
+            value (Any | None): The value to validate.
+            expected_type (type): The expected type of the value.
+            field_name (str): The name of the field being validated (for error messages).
+
+        Returns:
+            Any: The validated value if it is not None and matches the expected type.
+
+        Raises:
+            ValueError: If the value is None.
+            TypeError: If the value is not of the expected type.
+        """
+        if value is None:
+            error = f'{field_name} is required and cannot be None.'
+            raise ValueError(error)
+
+        return TrainModelParams._check_type(value, expected_type, field_name)
+
+    @staticmethod
+    def _check_type(value: Any | None, expected_type: type, field_name: str) -> Any:
+        """
+        Validate that a value matches the expected type.
+
+        This method checks type compatibility and raises a descriptive error
+        if the value does not match the expected type.
+
+        Args:
+            value (Any | None): The value to validate.
+            expected_type (type): The expected type of the value.
+            field_name (str): The name of the field being validated (for error messages).
+
+        Returns:
+            Any: The validated value if it matches the expected type.
+
+        Raises:
+            TypeError: If the value is not of the expected type.
+        """
+        if value is not None and not isinstance(value, expected_type):
+            error = f'{field_name} must be of type {expected_type.__name__}, but got {type(value).__name__}.'
+            raise TypeError(error)
+
+        return value
+
+    @staticmethod
+    def _coerce_experiment_run_id(value: Any) -> int:
+        """
+        Coerce experiment_run_id to int.
+
+        Workflow clients may send numeric strings; this keeps from_dict aligned with
+        workflow validation.
+
+        Args:
+            value: Raw experiment_run_id from the payload.
+
+        Returns:
+            int: Parsed experiment run id.
+
+        Raises:
+            ValueError: If the value is None.
+            TypeError: If the value cannot be coerced to a non-boolean integer.
+        """
+        if value is None:
+            raise ValueError('experiment_run_id is required and cannot be None.')
+        if isinstance(value, bool):
+            raise TypeError('experiment_run_id must be an integer, got bool.')
+        if isinstance(value, int):
+            return value
+        if isinstance(value, str) and value.strip().isdigit():
+            return int(value.strip())
+        if isinstance(value, float) and value.is_integer():
+            return int(value)
+        raise TypeError(
+            f'experiment_run_id must be an integer or numeric string, but got {type(value).__name__}.'
+        )
+
+    @staticmethod
+    def _parse_optional_model_metadata(value: Any) -> dict | None:
+        """
+        Parse model_metadata for from_dict before load_model_metadata fills the index.
+
+        Args:
+            value: model_metadata from the payload, or None if not sent yet.
+
+        Returns:
+            dict | None: Dict when provided; None when absent (filled later by load_model_metadata).
+
+        Raises:
+            TypeError: If value is neither None nor a dict.
+        """
+        if value is None:
+            return None
+        if isinstance(value, dict):
+            return value
+        raise TypeError(f'model_metadata must be a dict or None, but got {type(value).__name__}.')
+
+    def validate_business_rules(self) -> None:
+        """
+        Validate business rules and constraints for training parameters.
+
+        This method performs additional validation beyond type checking to ensure
+        that parameter values are within acceptable ranges and logically consistent.
+        It implements defense-in-depth validation to catch configuration errors
+        early in the workflow.
+
+        Raises:
+            ValueError: If any business rule is violated
+        """
+        self._validate_numeric_ranges()
+        self._validate_model_params()
+        self._validate_required_strings()
+        self._validate_date_format()
+
+    def _validate_numeric_ranges(self) -> None:
+        """Validate numeric parameters are within acceptable ranges."""
+        if not 10 <= self.train_size <= 100:
+            raise ValueError(f'train_size must be between 10 and 100, got {self.train_size}')
+
+        if not self.variable_columns:
+            raise ValueError('variable_columns cannot be empty')
+
+    def _validate_model_params(self) -> None:
+        """Validate model-related parameters."""
+        if not self.model_metadata:
+            raise ValueError('model_metadata is required')
+
+        schemas = self.model_metadata.get('schemas', {}).get('components', {}).get('schemas')
+
+        if not schemas:
+            return
+
+        data_model_schema = schemas.get('data_model')
+        model_schema = schemas.get('model')
+        opt_params_schema = schemas.get('opt_params')
+
+        if data_model_schema:
+            self._validate_model_param(data_model_schema, self.data_model_kwargs)
+        if model_schema:
+            self._validate_model_param(model_schema, self.model_kwargs)
+        if opt_params_schema:
+            self._validate_model_param(opt_params_schema, self.opt_params)
+
+    def _validate_model_param(self, schema: dict[str, Any], value: Any) -> None:
+        """Validate model parameter against schema."""
+        try:
+            validator = Draft202012Validator(schema)
+            validator.validate(value)
+        except ValidationError as e:
+            raise ValueError(f'Model parameters validation failed: {e.message}') from e
+
+    def _validate_required_strings(self) -> None:
+        """Validate required string fields are not empty."""
+        if not self.target_variable.strip():
+            raise ValueError('target_variable cannot be empty or whitespace')
+
+        if not self.bucket_name.strip():
+            raise ValueError('bucket_name cannot be empty or whitespace')
+
+        if not self.file_name.strip():
+            raise ValueError('file_name cannot be empty or whitespace')
+
+        if not self.model_name.strip():
+            raise ValueError('model_name cannot be empty or whitespace')
+
+        if not self.date_column.strip():
+            raise ValueError('date_column cannot be empty or whitespace')
+
+    def _validate_date_format(self) -> None:
+        """Validate date_format is one of the allowed frontend formats."""
+        validate_frontend_date_format(self.date_format)

model_manager/utils/models/train_model_result.py

@@ -0,0 +1,53 @@
+from dataclasses import dataclass
+
+import pandas as pd
+
+from model_manager.utils.models.train_model_params import TrainModelParams
+
+
+@dataclass
+class TrainModelResult:
+    """
+    A data container for storing the results of a machine learning training process.
+
+    This dataclass encapsulates all outputs from the training pipeline, including
+    the prepared datasets, evaluation metrics, and paths to generated artifacts.
+    It is used to pass results between activities in the training workflow.
+
+    Attributes:
+        params (TrainModelParams): The parameters used to train the model.
+        x_train (pd.DataFrame): The training dataset features.
+        x_test (pd.DataFrame): The testing dataset features.
+        y_train (pd.DataFrame): The training dataset target values.
+        y_test (pd.DataFrame): The testing dataset target values.
+        y_pred (pd.Series | None): The predicted target values for the testing dataset. Default is None.
+        y_train_pred (pd.Series | None): The predicted target values for the training dataset. Default is None.
+        mse_val (float | None): The Mean Squared Error (MSE) of the predictions. Default is None.
+        mae_val (float | None): The Mean Absolute Error (MAE) of the predictions. Default is None.
+        r2_val (float | None): The R-squared (R²) value of the predictions. Default is None.
+        equation (dict | None): The equation of the model. Default is None.
+        equation_path (str | None): The path to the equation file. Default is None.
+        run_name (str | None): The name of the MLFlow run. Default is None.
+        report_path (str | None): The path to the generated HTML report file. Default is None.
+        train_data_path (str | None): The path to the training dataset CSV file. Default is None.
+        test_data_path (str | None): The path to the testing dataset CSV file. Default is None.
+    """
+
+    params: TrainModelParams
+    train_data: pd.DataFrame
+    val_data: pd.DataFrame
+    y_pred: pd.DataFrame | None = None
+    y_train_pred: pd.DataFrame | None = None
+    mse_val: float | None = None
+    mae_val: float | None = None
+    r2_val: float | None = None
+    equation: dict | None = None
+    equation_path: str | None = None
+    run_name: str | None = None
+    experiment_name: str | None = None
+    run_id: str | None = None
+    report_path: str | None = None
+    train_data_path: str | None = None
+    test_data_path: str | None = None
+
+    run_dir: str | None = None

model_manager/utils/repository/data_manager_repository.py

@@ -0,0 +1,636 @@
+"""
+Data management repository for the training pipeline.
+
+This module provides the core data loading and preprocessing logic for the
+training pipeline, including:
+- CSV loading from in-memory bytes
+- datetime parsing and index configuration
+- optional support filters
+- train/test split management (when no explicit validation dataset is provided)
+
+It is intentionally decoupled from any specific model implementation or MLflow
+integration. Models are trained elsewhere (e.g., via SientiaModel wrappers),
+and this repository focuses solely on preparing data structures for them.
+"""
+
+import json
+from datetime import datetime
+from io import BytesIO
+from os import makedirs, path
+from shutil import rmtree
+from typing import Any
+
+import numpy as np
+import pandas as pd
+from sientia_do.observability.logger import Logger
+from sientia_do.observability.sientia_monitoring import SientiaMonitoring
+from sientia_model.wrappers.sientia_model import SientiaModel
+
+from model_manager.runtime_paths import PROJECT_BASE_PATH, REPORTS_ROOT
+from model_manager.sientia.metrics import mae, mse, r2
+from model_manager.sientia.reports import Reports  # type: ignore[import-untyped]
+from model_manager.utils.models.train_model_params import (
+    FRONTEND_DATE_FORMAT_TO_STRFTIME,
+    TrainModelParams,
+)
+from model_manager.utils.models.train_model_result import TrainModelResult
+
+
+def train_test_split(
+    data: pd.DataFrame,
+    train_size: float,
+    random_state: int | None = None,
+    shuffle: bool = True,
+) -> tuple[pd.DataFrame, pd.DataFrame]:
+    # 1. Definir a semente (seed) para reprodutibilidade
+    if random_state is not None:
+        np.random.seed(random_state)
+
+    # 2. Gerar índices e embaralhar se necessário
+    indices = np.arange(len(data))
+
+    if shuffle:
+        np.random.shuffle(indices)
+
+    # 3. Calcular o ponto de corte (split point)
+    # Cálculo: N_treino = tamanho_total * proporcao_treino
+    n_train = int(len(data) * train_size)
+
+    # 4. Dividir os índices
+    train_indices = indices[:n_train]
+    test_indices = indices[n_train:]
+
+    # 5. Retornar os dados fatiados
+    return data.iloc[train_indices], data.iloc[test_indices]
+
+
+def _ensure_date_column_parsed(data: pd.DataFrame, params: TrainModelParams) -> pd.DataFrame:
+    """
+    Parse params.date_column using the frontend date_format mapping only.
+
+    date_column must exist in ``data`` (callers validate before prepare). No broad
+    pandas inference or alternate timezone formats here—clients must send a supported
+    date_format or rely on the TrainModelParams default.
+    """
+    if params.date_column not in data.columns:
+        raise ValueError(
+            f'date_column "{params.date_column}" not found in dataset columns: {list(data.columns)}'
+        )
+    data = data.copy()
+    col = data[params.date_column]
+    if params.date_format not in FRONTEND_DATE_FORMAT_TO_STRFTIME:
+        raise ValueError(
+            f'date_format "{params.date_format}" is not mapped to a strftime pattern '
+            '(must be one of the allowed frontend formats).'
+        )
+    strf = FRONTEND_DATE_FORMAT_TO_STRFTIME[params.date_format]
+    try:
+        parsed = pd.to_datetime(col, format=strf, errors='raise')
+        data[params.date_column] = parsed
+    except Exception as e:
+        raise ValueError(
+            f'Failed to parse date column "{params.date_column}" with format "{params.date_format}": {e}'
+        ) from e
+    return data
+
+
+class DataManagerRepository(SientiaMonitoring):
+    """
+    Repository for data preparation in the training pipeline.
+
+    This class encapsulates the core logic for preparing ML training data:
+    loading CSV bytes, applying date/index configuration, support filters, and
+    constructing train/test splits (or using an explicit validation dataset).
+
+    Attributes:
+        logger (Logger): Logger instance for observability and debugging
+    """
+
+    def __init__(self, logger: Logger):
+        """
+        Initialize DataManagerRepository with logger.
+
+        Args:
+            logger: Logger instance for observability
+        """
+        SientiaMonitoring.__init__(
+            self,
+            logger=logger,
+            notification_handler=None,
+            metrics_controller=None,
+        )
+
+    def _drop_rows_with_missing_timestamp(
+        self,
+        df: pd.DataFrame,
+        params: TrainModelParams,
+        metadata: dict[str, Any] | None,
+    ) -> pd.DataFrame:
+        """
+        Remove rows where the configured date_column is missing (NaN/NaT/blank string).
+
+        Empty timestamp cells cannot be placed on a DatetimeIndex and break
+        downstream joins and metrics.
+        """
+        if params.date_column not in df.columns:
+            return df
+        series = df[params.date_column]
+        mask = series.notna()
+        if series.dtype == object:
+            stripped = series.astype(str).str.strip()
+            mask &= stripped.ne('')
+            mask &= stripped.str.lower().ne('nan')
+        n_drop = int((~mask).sum())
+        if n_drop:
+            self.info(
+                f'Dropping {n_drop} row(s) with missing or blank timestamp column '
+                f'"{params.date_column}"',
+                metadata,
+            )
+        return df.loc[mask].copy()
+
+    def _coerce_non_timestamp_columns_to_numeric(
+        self,
+        df: pd.DataFrame,
+        params: TrainModelParams,
+        metadata: dict[str, Any] | None,
+    ) -> pd.DataFrame:
+        """
+        Coerce all non-timestamp columns to numeric dtype.
+
+        The timestamp column defined by params.date_column is excluded from coercion.
+        Non-numeric values are coerced to NaN.
+        """
+        out = df.copy()
+        for col in out.columns:
+            if col == params.date_column:
+                continue
+            original_na = int(out[col].isna().sum())
+            out[col] = pd.to_numeric(out[col], errors='coerce')
+            new_na = int(out[col].isna().sum())
+            introduced_na = new_na - original_na
+            if introduced_na > 0:
+                self.warning(
+                    f'Column "{col}" had {introduced_na} non-numeric value(s) coerced to NaN',
+                    metadata,
+                )
+        return out
+
+    def prepare_training_data(
+        self,
+        train_file_bytes: bytes,
+        validation_file_bytes: bytes | None,
+        params: TrainModelParams,
+        metadata: dict[str, Any] | None = None,
+    ) -> TrainModelResult:
+        """
+        Build TrainModelResult from raw CSV bytes for train (and optional validation) data.
+
+        This method orchestrates the data pipeline:
+        1. Load training data from in-memory bytes
+        2. Optionally load validation data from in-memory bytes
+        3. Parse and configure datetime index
+        4. Apply optional support filters
+        5. Split into train/test sets when no explicit validation dataset is provided
+
+        Args:
+            train_file_bytes: Raw bytes of the training CSV.
+            validation_file_bytes: Raw bytes of the validation CSV, or None when
+                validation should be derived via train/test split.
+            params: Training parameters (TrainModelParams).
+
+        Returns:
+            TrainModelResult: Object containing processed data, train/test splits,
+            and scaler dictionary.
+
+        Raises:
+            ValueError: If transformed data is empty.
+            Exception: If data loading or preprocessing fails.
+        """
+        try:
+            train_df = pd.read_csv(
+                BytesIO(train_file_bytes),
+                sep=params.line_separator,
+                decimal=params.decimal_separator,
+            )
+        except Exception as exc:  # noqa: BLE001
+            raise ValueError(
+                'Failed to load training CSV data from MinIO object. '
+                'Check file encoding, line separator and decimal separator.'
+            ) from exc
+
+        train_df = self._drop_rows_with_missing_timestamp(train_df, params, metadata)
+        train_df = _ensure_date_column_parsed(train_df, params)
+        train_df = self._configure_datetime_index(train_df, params, metadata)
+        train_df = self._set_timezone_on_index(train_df, metadata)
+        train_df = self._coerce_non_timestamp_columns_to_numeric(train_df, params, metadata)
+
+        if len(train_df) <= 0:
+            raise ValueError('Training data view is empty after transformation')
+
+        # Explicit validation dataset path
+        train_data = pd.DataFrame(train_df[params.variable_columns + [params.target_variable]])
+        if validation_file_bytes is not None:
+            try:
+                val_df = pd.read_csv(
+                    BytesIO(validation_file_bytes),
+                    sep=params.line_separator,
+                    decimal=params.decimal_separator,
+                )
+            except Exception as exc:  # noqa: BLE001
+                raise ValueError(
+                    'Failed to load validation CSV data from MinIO object. '
+                    'Check file encoding, line separator and decimal separator.'
+                ) from exc
+
+            val_df = self._drop_rows_with_missing_timestamp(val_df, params, metadata)
+            val_df = _ensure_date_column_parsed(val_df, params)
+            val_df = self._configure_datetime_index(val_df, params, metadata)
+            val_df = self._set_timezone_on_index(val_df, metadata)
+            val_df = self._coerce_non_timestamp_columns_to_numeric(val_df, params, metadata)
+
+            if len(val_df) <= 0:
+                raise ValueError('Validation data view is empty after transformation')
+
+            val_data = pd.DataFrame(val_df[params.variable_columns + [params.target_variable]])
+        else:
+            # Fallback path: derive validation via train/test split from a single dataset.
+            train_data, val_data = train_test_split(
+                train_data,
+                train_size=params.train_size / 100,
+                shuffle=params.shuffle,
+                random_state=params.random_state,
+            )
+
+        self.info(
+            f'Data preprocessed and split successfully - experiment run id: {params.experiment_run_id}',
+            metadata,
+        )
+
+        experiment_name = f'{params.experiment_name}'
+        run_name = f'{experiment_name}_{datetime.now().strftime("%Y%m%d_%H%M%S")}'
+
+        return TrainModelResult(
+            params=params,
+            train_data=train_data,
+            val_data=val_data,
+            run_name=run_name,
+            experiment_name=experiment_name,
+        )
+
+    def _as_series(self, pred: pd.DataFrame | pd.Series) -> pd.Series:
+        if isinstance(pred, pd.Series):
+            return pred
+        # If the wrapper returns a single-column DataFrame, take its first column.
+        if pred.shape[1] == 1:
+            return pred.iloc[:, 0]
+        raise ValueError('y_pred/y_train_pred must be a Series or single-column DataFrame')
+
+    def _extract_model_equation(self, regr: Any, params: TrainModelParams) -> dict:
+        """
+        Extract the linear regression equation coefficients and create equation metadata.
+
+        This method extracts the coefficients and intercept from the trained model
+        and creates a structured dictionary containing the equation information
+        for serialization as JSON artifact.
+
+        Args:
+            regr: Trained LinearRegressionModel object
+            params: Training parameters containing variable information
+
+        Returns:
+            dict: Equation metadata containing:
+                - target_variable: Name of the target variable
+                - coefficients: Dictionary mapping variable names to coefficients
+                - intercept: Model intercept value
+                - equation_string: Human-readable equation string
+                - latex_equation: LaTeX formatted equation
+        """
+        coefficients = regr.regr.coef_
+        intercept = regr.regr.intercept_
+
+        # Get feature names - for polynomial models, use poly_feature_names
+        model_kwargs = params.model_kwargs or {}
+        degree = model_kwargs.get('degree', 1)
+        poly_feature_names = model_kwargs.get('poly_feature_names', None)
+
+        if degree > 1 and poly_feature_names:
+            feature_names = poly_feature_names
+        else:
+            feature_names = params.variable_columns
+
+        # Create coefficients dictionary
+        coefficients_dict = {}
+        for i, var in enumerate(feature_names):
+            if i < len(coefficients):
+                coefficients_dict[var] = float(coefficients[i])
+
+        # Create equation string
+        equation_parts = [f'{coef:.6f} * {var}' for var, coef in coefficients_dict.items()]
+        equation_string = f'{params.target_variable} = {intercept:.6f} + ' + ' + '.join(
+            equation_parts
+        )
+
+        # Create LaTeX equation
+        latex_parts = [f'{coef:.6f} \\cdot {var}' for var, coef in coefficients_dict.items()]
+        latex_equation = f'{params.target_variable} = {intercept:.6f} + ' + ' + '.join(latex_parts)
+
+        return {
+            'target_variable': params.target_variable,
+            'coefficients': coefficients_dict,
+            'intercept': float(intercept),
+            'equation_string': equation_string,
+            'latex_equation': latex_equation,
+            'model_type': params.model_name,
+            'degree': degree,
+            'interaction_only': model_kwargs.get('interaction_only', False),
+            'original_features': feature_names,
+        }
+
+    def compute_regression_metrics(
+        self,
+        tmr: TrainModelResult,
+        wrapper: SientiaModel,
+        metadata: dict[str, Any] | None = None,
+    ) -> TrainModelResult:
+        """
+        Compute regression metrics for training results.
+
+        This helper mirrors the previous TrainingRepository.after_train_calculation
+        behavior, assuming that predictions (y_pred/y_train_pred) are already on the
+        correct scale for metric calculation (any scaling is handled inside the
+        model wrapper).
+
+        Args:
+            tmr: Training result containing:
+                - train_data/val_data DataFrames with a target column
+                - y_train_pred/y_pred populated (model predictions for train/val)
+            wrapper: Trained model wrapper (used for linear equation extraction).
+            metadata: Optional workflow metadata for debug logging.
+
+        Return:
+            TrainModelResult: Same object with mse_val, mae_val and r2_val set.
+        """
+        if tmr.y_pred is None:
+            raise ValueError('y_pred must be set before computing regression metrics')
+
+        params = tmr.params
+        target = params.target_variable
+
+        # True values are expected to come from val_data.
+        y_true_val = tmr.val_data[target]
+
+        y_pred_val = self._as_series(tmr.y_pred).sort_index()
+        y_true_val = y_true_val.sort_index()
+
+        # Align by index to avoid metric calculation errors if ordering differs.
+        common_index = y_true_val.index.intersection(y_pred_val.index)
+        head = min(5, len(y_true_val), len(y_pred_val))
+        self.debug(
+            'compute_regression_metrics index alignment: '
+            f'val_n={len(y_true_val)} pred_n={len(y_pred_val)} common_n={len(common_index)}; '
+            f'val_index_dtype={y_true_val.index.dtype} '
+            f'pred_index_dtype={y_pred_val.index.dtype}; '
+            f'val_index_sample={list(y_true_val.index[:head])} '
+            f'pred_index_sample={list(y_pred_val.index[:head])}',
+            metadata,
+        )
+
+        if len(common_index) == 0:
+            raise ValueError(
+                'No overlapping indices between val_data and y_pred. '
+                f'val_n={len(y_true_val)} pred_n={len(y_pred_val)} '
+                f'val_index_sample={list(y_true_val.index[:head])} '
+                f'pred_index_sample={list(y_pred_val.index[:head])}'
+            )
+
+        y_true_val = y_true_val.loc[common_index]
+        y_pred_val = y_pred_val.loc[common_index]
+
+        # Metrics helpers already round to 2 decimals.
+        tmr.mse_val = mse(y_true_val, y_pred_val)
+        tmr.mae_val = mae(y_true_val, y_pred_val)
+        tmr.r2_val = r2(y_true_val, y_pred_val)
+
+        if params.model_type == 'linear_regression':
+            inner = getattr(wrapper, 'model', None)
+            regr = getattr(inner, 'regr', None) if inner is not None else None
+            if regr is not None and hasattr(regr, 'coef_') and hasattr(regr, 'intercept_'):
+                tmr.equation = self._extract_model_equation(inner, params)
+
+        return tmr
+
+    def _configure_datetime_index(
+        self,
+        data: pd.DataFrame | None,
+        params: TrainModelParams,
+        metadata: dict[str, Any] | None = None,
+    ) -> pd.DataFrame:
+        """
+        Configure datetime index for the DataFrame.
+
+        Guards against None to avoid 'NoneType' object has no attribute 'index' downstream.
+        Uses only params.date_column and assumes it was already parsed exactly once by
+        _ensure_date_column_parsed.
+        Args:
+            data: The DataFrame to configure the datetime index for.
+            params: The training parameters.
+            metadata: The metadata for the training run.
+
+        Returns:
+            The DataFrame with the datetime index configured.
+        """
+        if data is None:
+            raise ValueError(
+                'Data is None after load_data. '
+                'Check file format, line separator and decimal separator.'
+            )
+
+        if params.date_column not in data.columns:
+            raise ValueError(
+                f'date_column "{params.date_column}" not found in dataset columns: {list(data.columns)}'
+            )
+
+        if not pd.api.types.is_datetime64_any_dtype(data[params.date_column]):
+            raise ValueError(
+                f'date_column "{params.date_column}" must be datetime before index configuration'
+            )
+
+        data = data.set_index(params.date_column)
+        data = data.sort_index()
+        self.info(f'Configured datetime index from column: {params.date_column}', metadata)
+        return data
+
+    def _set_timezone_on_index(
+        self, data: pd.DataFrame, metadata: dict[str, Any] | None = None
+    ) -> pd.DataFrame:
+        """
+        Check if the index has a timezone and if not, set it to UTC timezone.
+
+        Args:
+            data: The DataFrame to set the timezone on.
+            metadata: The metadata for the training run.
+
+        Returns:
+            The DataFrame with the timezone set.
+        """
+
+        if isinstance(data.index, pd.DatetimeIndex):
+            if data.index.tz is None:
+                data.index = data.index.tz_localize('UTC')
+            else:
+                data.index = data.index.tz_convert('UTC')
+        else:
+            raise ValueError('Index is not a DatetimeIndex')
+
+        return data
+
+    def _get_reports_directory(self) -> str:
+        """
+        Get the absolute path to the reports directory.
+
+        Returns:
+            str: Absolute path to the runtime reports root.
+        """
+        return REPORTS_ROOT
+
+    def _create_run_directory(
+        self, base_path: str, run_name: str, metadata: dict[str, Any] | None = None
+    ) -> str:
+        """
+        Creates a directory inside the 'reports' folder with the run name and a timestamp.
+
+        Uses microsecond precision in timestamp to minimize collision probability
+        in high-concurrency scenarios.
+
+        Args:
+            base_path (str): The path to the 'reports' folder.
+            run_name (str): The name of the run.
+
+        Returns:
+            str: The path to the created directory.
+
+        Raises:
+            PermissionError: If there are insufficient permissions to create the directory.
+            OSError: If directory creation fails for any other reason.
+        """
+        # Use microsecond precision to reduce collision probability
+        run_dir = path.join(base_path, 'temp', f'{run_name}')
+
+        try:
+            makedirs(run_dir, exist_ok=True)
+            return run_dir
+        except PermissionError as e:
+            error_msg = f'Permission denied when creating directory: {run_dir}'
+            self.error(error_msg, metadata)
+            raise PermissionError(error_msg) from e
+        except OSError as e:
+            error_msg = f'Failed to create directory {run_dir}: {str(e)}'
+            self.error(error_msg, metadata)
+            raise OSError(error_msg) from e
+
+    def generate_report(
+        self, data: TrainModelResult, metadata: dict[str, Any] | None = None
+    ) -> TrainModelResult:
+        """
+        Generates a comprehensive report summarizing data quality, data drift, and regression analysis.
+
+        Args:
+            reference_data (pd.DataFrame): The training dataset with predictions added.
+            current_data (pd.DataFrame): The testing dataset with predictions added.
+            data: The training model result containing the datasets, model, and parameters.
+
+        Returns:
+            The updated result object with paths to the generated report and data files.
+
+        Raises:
+            ValueError: If data conversion to float64 fails or DataFrames are invalid.
+            PermissionError: If there are insufficient permissions to write files.
+            OSError: If file writing fails for any other reason.
+        """
+
+        if data.run_name is None:
+            raise ValueError('run_name is not set, cannot generate report')
+
+        if data.y_train_pred is None or data.y_pred is None:
+            raise ValueError('y_train_pred or y_pred is not set, cannot generate report')
+
+        y_train_pred = data.y_train_pred.rename(columns={data.params.target_variable: 'prediction'})
+        y_val_pred = data.y_pred.rename(columns={data.params.target_variable: 'prediction'})
+
+        # Join the predictions to the data
+        reference_data = y_train_pred[['prediction']].join(data.train_data, how='inner')
+        reference_data_float = reference_data.astype(np.float64)
+
+        current_data = y_val_pred[['prediction']].join(data.val_data, how='inner')
+        current_data_float = current_data.astype(np.float64)
+
+        # Evidently's ConflictTargetMetric expects a literal `target` column name.
+        # Keep the original target column and provide this alias for report metrics.
+        target_col = data.params.target_variable
+
+        reference_data_float['target'] = reference_data_float[target_col]
+        current_data_float['target'] = current_data_float[target_col]
+
+        # Initialize report generator
+        base_path = self._get_reports_directory()
+        data.run_dir = self._create_run_directory(base_path, data.run_name)
+
+        # Template path is the code path of the model_manager package
+        template_path = path.join(PROJECT_BASE_PATH, 'reports')
+
+        report = Reports(
+            reference_data=reference_data_float,
+            current_data=current_data_float,
+            base_path=data.run_dir,
+            template_path=template_path,
+            target_name=data.params.target_variable,
+        )
+
+        # Generate report sections
+        feature_and_target_cols = data.params.variable_columns + [target_col]
+        report.add_data_quality_section(columns=feature_and_target_cols)
+        report.add_data_drift_section(columns=feature_and_target_cols)
+        report.add_regression_section()
+
+        # Save HTML report
+        data.report_path = path.join(data.run_dir, 'report.html')
+        report.save_all_sections_html(data.report_path)
+
+        # Save training / validation CSVs using the same frames as the report (includes
+        # literal `target` alias for Evidently, plus predictions and float-cast features).
+        data.train_data_path = path.join(data.run_dir, 'train_data.csv')
+        reference_data_float.to_csv(data.train_data_path, index=False)
+
+        # Save test data CSV
+        data.test_data_path = path.join(data.run_dir, 'test_data.csv')
+        current_data_float.to_csv(data.test_data_path, index=False)
+
+        # Save equation as JSON
+        if data.equation is not None and data.params.model_type == 'linear_regression':
+            data.equation_path = path.join(data.run_dir, 'model_equation.json')
+            with open(data.equation_path, 'w', encoding='utf-8') as f:
+                json.dump(data.equation, f, indent=2, ensure_ascii=False)
+
+        return data
+
+    def cleanup_run_directory(self, run_dir: str, metadata: dict[str, Any] | None = None) -> None:
+        """
+        Clean up temporary run directory after model training.
+
+        This activity deletes the temporary directory created during model training
+        and artifact generation. It implements idempotent cleanup to handle cases
+        where the directory may have already been deleted.
+
+        Args:
+            run_dir (str): Path to the run directory to delete
+        """
+        if not run_dir:
+            self.info('No run directory specified, skipping cleanup')
+            return
+
+        if path.exists(run_dir):
+            rmtree(run_dir)
+            self.info(f'Run directory deleted successfully: {run_dir}')
+        else:
+            self.info(f'Run directory already deleted: {run_dir}')

model_manager/worker/prepare_worker.py

@@ -0,0 +1,119 @@
+import os
+import re
+from collections.abc import Sequence
+from concurrent.futures import ThreadPoolExecutor
+from typing import Any
+
+from sientia_do.observability.logger import Logger
+from temporalio.client import Client
+from temporalio.worker import PollerBehaviorAutoscaling, Worker
+
+# Worker configuration parameters with default values.
+parameters = [
+    ('MAX_CONCURRENT_WORKFLOW_TASKS', '200'),
+    ('MAX_CONCURRENT_ACTIVITIES', '200'),
+    ('MAX_CONCURRENT_LOCAL_ACTIVITIES', '200'),
+    ('MAX_CACHED_WORKFLOWS', '200'),
+    ('WORKFLOW_POLLER_BEHAVIOUR_MINIMUM', '10'),
+    ('WORKFLOW_POLLER_BEHAVIOUR_INITIAL', '100'),
+    ('WORKFLOW_POLLER_BEHAVIOUR_MAXIMUM', '200'),
+    ('ACTIVITY_POLLER_BEHAVIOUR_MINIMUM', '10'),
+    ('ACTIVITY_POLLER_BEHAVIOUR_INITIAL', '100'),
+    ('ACTIVITY_POLLER_BEHAVIOUR_MAXIMUM', '200'),
+    ('ACTIVITY_EXECUTOR_MAX_WORKERS', '200'),
+]
+
+
+def camel_to_snake(text: str) -> str:
+    """
+    Convert a CamelCase or camelCase string into snake_case.
+
+    Args:
+     - text: str, original string in CamelCase or camelCase format
+
+    Return:
+     str: converted string in snake_case format
+    """
+    text = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', text)
+    text = re.sub('([a-z0-9])([A-Z])', r'\1_\2', text)
+    return text.lower()
+
+
+def build_queue_name(workflow_name: str, runtime: str | None = None) -> str:
+    """
+    Build Temporal queue name from workflow name and runtime.
+
+    Args:
+     - workflow_name: str, workflow class name in CamelCase format
+     - runtime: str | None, runtime suffix for environment-specific queues
+
+    Return:
+     str: queue name in the format <workflow>-<runtime>-queue or <workflow>-queue
+    """
+    snake_workflow_name = camel_to_snake(workflow_name)
+    if runtime:
+        return f'{snake_workflow_name}-{runtime}-queue'
+    return f'{snake_workflow_name}-queue'
+
+
+def prepare_worker(
+    main_workflow: type,
+    other_workflows: Sequence[type],
+    activities: Sequence[Any],
+    temporal_client: Client,
+    logger: Logger,
+    runtime: str | None = None,
+) -> Worker:
+    """
+    Build and configure a Temporal worker for the given workflow and activities.
+
+    Args:
+     - main_workflow: type, main workflow class used as worker entry point
+     - other_workflows: Sequence[type], additional workflows in the same worker
+     - activities: Sequence[Any], activity callables registered in this worker
+     - temporal_client: Client, Temporal client used by the worker
+     - logger: Logger, logger instance used during worker preparation
+     - runtime: str | None, runtime suffix appended to queue name when present
+
+    Return:
+     Worker: fully configured Temporal worker instance ready to run
+    """
+    main_workflow_name = main_workflow.__name__.upper()
+    queue_name = build_queue_name(main_workflow.__name__, runtime)
+
+    local_workflow_parameters: dict[str, int] = {}
+    for parameter_name, default_value in parameters:
+        local_workflow_parameters[parameter_name] = int(
+            os.getenv(f'{main_workflow_name}_{parameter_name}', default_value)
+        )
+
+    logger.info(f'Preparing worker for {main_workflow_name} with queue {queue_name}')
+
+    activity_executor = ThreadPoolExecutor(
+        max_workers=local_workflow_parameters['ACTIVITY_EXECUTOR_MAX_WORKERS'],
+        thread_name_prefix=f'{queue_name}-activity',
+    )
+
+    return Worker(
+        temporal_client,
+        task_queue=queue_name,
+        workflows=[main_workflow, *other_workflows],
+        activities=[*activities],
+        activity_executor=activity_executor,
+        max_concurrent_workflow_tasks=local_workflow_parameters['MAX_CONCURRENT_WORKFLOW_TASKS'],
+        max_concurrent_activities=local_workflow_parameters['MAX_CONCURRENT_ACTIVITIES'],
+        max_concurrent_local_activities=local_workflow_parameters[
+            'MAX_CONCURRENT_LOCAL_ACTIVITIES'
+        ],
+        max_cached_workflows=local_workflow_parameters['MAX_CACHED_WORKFLOWS'],
+        workflow_task_poller_behavior=PollerBehaviorAutoscaling(
+            minimum=local_workflow_parameters['WORKFLOW_POLLER_BEHAVIOUR_MINIMUM'],
+            initial=local_workflow_parameters['WORKFLOW_POLLER_BEHAVIOUR_INITIAL'],
+            maximum=local_workflow_parameters['WORKFLOW_POLLER_BEHAVIOUR_MAXIMUM'],
+        ),
+        activity_task_poller_behavior=PollerBehaviorAutoscaling(
+            minimum=local_workflow_parameters['ACTIVITY_POLLER_BEHAVIOUR_MINIMUM'],
+            initial=local_workflow_parameters['ACTIVITY_POLLER_BEHAVIOUR_INITIAL'],
+            maximum=local_workflow_parameters['ACTIVITY_POLLER_BEHAVIOUR_MAXIMUM'],
+        ),
+    )

model_manager/worker/worker.py

@@ -0,0 +1,259 @@
+"""Model Manager Worker Module
+
+This module provides the main worker implementation for the Sientia DataOps Model Manager system.
+It orchestrates Temporal workers, manages task queues, and handles the lifecycle of
+model training and cleanup workflows.
+
+The worker supports two task queues:
+- train_model-<runtime>-queue: For ML model training workflows
+- cleanup_files-<runtime>-queue: For file cleanup workflows
+
+Key Features:
+- Automatic scaling with PollerBehaviorAutoscaling
+- Prometheus metrics integration
+- Comprehensive error handling and logging
+- Graceful shutdown with cleanup
+- ML model training pipeline orchestration
+- Automated cleanup schedule management
+
+Environment Variables:
+- TEMPORAL_HOST: Temporal server address (default: localhost:7233)
+- TEMPORAL_NAMESPACE: Temporal namespace (default: model-manager)
+- TEMPORAL_USE_TLS: Enable TLS for Temporal connection (default: false)
+- RUNTIME: Runtime identifier used in queue naming (default: single)
+- POD_ID: Kubernetes pod identifier for metrics
+- HTTP_METRICS_PORT: Prometheus metrics server port (default: 9090)
+- HTTP_SDK_METRICS_PORT: Temporal SDK metrics port (default: 9091)
+- PROJECT_NAME: Project name for notifications (default: model-manager)
+"""
+
+from temporalio import client, workflow
+from temporalio.runtime import PrometheusConfig, Runtime, TelemetryConfig
+
+with workflow.unsafe.imports_passed_through():
+    import asyncio
+    import os
+    import sys
+
+    from prometheus_client import start_http_server
+    from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
+    from sientia_do.observability.logger import Logger as SientiaLogger
+    from sientia_do.observability.metrics_controller import MetricsController
+    from sientia_model.model_repository.plugin_store import PluginStore
+
+    from model_manager import metrics
+    from model_manager.activities.activities import Activities
+    from model_manager.runtime_paths import ensure_runtime_directories
+    from model_manager.schedules.cleanup_schedule import create_cleanup_schedule
+    from model_manager.utils.connectors_config import (
+        build_minio_config,
+        build_mlflow_config,
+        build_mongodb_config,
+        build_plugin_store_config,
+        build_postgres_config,
+    )
+    from model_manager.utils.logger_helper import get_logger
+    from model_manager.worker.prepare_worker import prepare_worker
+    from model_manager.workflows.cleanup_files import CleanupFiles
+    from model_manager.workflows.train_model import TrainModel
+
+POD_ID = os.getenv('POD_ID')
+RUNTIME = os.getenv('RUNTIME')
+SDK_METRICS_PORT = int(os.getenv('HTTP_SDK_METRICS_PORT', '9091'))
+
+
+def _get_runtime(runtime: str | None) -> str:
+    """
+    Resolve runtime using fallback when missing.
+
+    Args:
+     - runtime: str | None, runtime value from environment
+
+    Return:
+     str: normalized runtime value
+    """
+    normalized_runtime = runtime.strip() if runtime else ''
+    return normalized_runtime or 'single'
+
+
+async def main():
+    """
+    Main entry point for the Model Manager worker application.
+
+    This function initializes and starts all components of the worker:
+    1. Sets up logging and metadata
+    2. Starts Prometheus metrics server
+    3. Initializes notification handler
+    4. Creates and configures activities
+    5. Starts Temporal client and workers
+    6. Manages worker lifecycle and graceful shutdown
+
+    The function runs indefinitely until interrupted or an error occurs.
+    On error, it performs cleanup and exits with a non-zero status code.
+
+    Raises:
+        Exception: Any unhandled exception during worker execution
+        SystemExit: On graceful shutdown or error conditions
+    """
+
+    runtime = _get_runtime(RUNTIME)
+
+    ensure_runtime_directories()
+
+    host = os.getenv('TEMPORAL_HOST', 'localhost:7233')
+    use_tls = os.getenv('TEMPORAL_USE_TLS', 'false').lower() == 'true'
+    logger = get_logger(__name__)
+
+    metadata = {
+        'pod_id': POD_ID,
+        'runtime': runtime,
+    }
+
+    start_prometheus_server(logger, metadata)
+    mongo_config = build_mongodb_config()
+
+    notification_handler = NotificationHandler(
+        connection_string=mongo_config['connection_string'],
+        database=mongo_config['database_name'],
+        logger=logger,
+        project_name=os.getenv('PROJECT_NAME', 'model-manager'),
+    )
+
+    logger.custom_info(f'MongoDB client initialized at {mongo_config["uri"]}', metadata)
+
+    logger.custom_info('Initializing metrics controller', metadata)
+
+    metrics_controller = MetricsController(logger=logger)
+
+    logger.custom_info(f'Installing runtime {runtime}', metadata)
+
+    plugin_store_parameters = build_plugin_store_config()
+    plugin_store = PluginStore(
+        base_url=plugin_store_parameters['base_url'],
+        owner=plugin_store_parameters['owner'],
+        repo=plugin_store_parameters['repo'],
+        username=plugin_store_parameters['username'],
+        password=plugin_store_parameters['password'],
+        branch=plugin_store_parameters['branch'],
+        cache_ttl_seconds=plugin_store_parameters['cache_ttl_seconds'],
+        pypi_index_url=plugin_store_parameters['pypi_index_url'],
+        pypi_username=plugin_store_parameters['pypi_username'],
+        pypi_password=plugin_store_parameters['pypi_password'],
+        logger=logger,
+        notification_handler=notification_handler,
+        metrics_controller=metrics_controller,
+    )
+
+    await plugin_store.install_runtime(runtime_name=runtime)
+
+    activities = Activities(
+        postgres_config=build_postgres_config(),
+        mlflow_config=build_mlflow_config(),
+        minio_config=build_minio_config(),
+        plugin_store=plugin_store,
+        logger=logger,
+        notification_handler=notification_handler,
+        metrics_controller=metrics_controller,
+    )
+
+    new_runtime = Runtime(
+        telemetry=TelemetryConfig(
+            metrics=PrometheusConfig(bind_address=f'0.0.0.0:{SDK_METRICS_PORT}')
+        )
+    )
+
+    logger.custom_info(f'SDK metrics server initialized on port {SDK_METRICS_PORT}', metadata)
+
+    namespace = os.getenv('TEMPORAL_NAMESPACE', 'model-manager')
+    temporal_client = await client.Client.connect(
+        target_host=host,
+        namespace=namespace,
+        runtime=new_runtime,
+        tls=use_tls,
+    )
+
+    logger.custom_info(f'Temporal client initialized at {host}/{namespace}', metadata)
+
+    # Create cleanup schedule (idempotent - only creates if doesn't exist)
+    try:
+        await create_cleanup_schedule(temporal_client, logger, metadata)
+    except Exception as e:  # noqa: BLE001
+        logger.custom_error(f'Failed to configure cleanup schedule: {e}', metadata)
+        # Don't fail the worker startup if schedule creation fails
+        # The schedule can be created manually if needed
+
+    workers = [
+        prepare_worker(
+            main_workflow=TrainModel,
+            other_workflows=[],
+            activities=[
+                activities.update_experiment_run,
+                activities.load_model_metadata,
+                activities.validate_train_params,
+                activities.train_model,
+                activities.cleanup_resources,
+            ],
+            temporal_client=temporal_client,
+            logger=logger,
+            runtime=runtime,
+        ),
+        prepare_worker(
+            main_workflow=CleanupFiles,
+            other_workflows=[],
+            activities=[
+                activities.cleanup_temp_directories,
+            ],
+            temporal_client=temporal_client,
+            logger=logger,
+            runtime=runtime,
+        ),
+    ]
+
+    handlers = [w.run() for w in workers]
+
+    logger.custom_info('Model manager workers initialized', metadata)
+
+    try:
+        # This will run the workers and wait for them to complete.
+        # If an exception occurs in any of the worker handlers, it will be propagated here.
+        await asyncio.gather(*handlers)
+    except BaseException as e:  # noqa: BLE001
+        logger.custom_error(f'An unhandled exception occurred: {e}', metadata)
+    finally:
+        notification_handler.shutdown()
+        logger.custom_info('MongoDB client closed', metadata)
+        activities.shutdown()
+        # Exit with a non-zero status code to indicate failure to Kubernetes
+        metrics.APP_UP.labels(pod_id=POD_ID).set(0)  # Mark app as DOWN
+        sys.exit(1)
+
+
+def start_prometheus_server(logger: SientiaLogger, metadata: dict[str, str | None]):
+    """
+    Starts the Prometheus metrics server for monitoring and observability.
+
+    This function initializes the Prometheus HTTP server on the configured port
+    and sets the application health metric to indicate the service is running.
+
+    The server exposes metrics that can be scraped by Prometheus for monitoring
+    the health and performance of the Model Manager worker.
+
+    Environment Variables:
+        HTTP_METRICS_PORT: Port for the metrics server (default: 9090)
+        POD_ID: Pod identifier for metrics labeling
+
+    Raises:
+        SystemExit: If the metrics server fails to start
+    """
+    try:
+        port = int(os.getenv('HTTP_METRICS_PORT', 9090))
+        start_http_server(port)
+        logger.custom_info(f'Prometheus server initialized on port {port}.', metadata)
+        metrics.APP_UP.labels(pod_id=POD_ID).set(1)  # Mark app as UP
+    except Exception as e:  # noqa: BLE001
+        logger.custom_critical(f'Failed to start Prometheus server: {e}', metadata)
+        os._exit(1)
+
+
+if __name__ == '__main__':
+    asyncio.run(main())

model_manager/workflows/cleanup_files.py

@@ -0,0 +1,64 @@
+"""
+Cleanup workflow for removing local filesystem.
+
+This module provides a Temporal cron workflow that runs daily to clean up
+temporary files and directories older than the configured retention period.
+"""
+
+from temporalio import workflow
+
+with workflow.unsafe.imports_passed_through():
+    import os
+    from datetime import timedelta
+    from typing import Any
+
+    from model_manager.activities.activities import Activities
+    from model_manager.runtime_paths import REPORTS_TEMP_DIR
+    from model_manager.workflows.train_model import no_retry_policy
+
+    TIMEOUT_CLEANUP_LOCAL = int(os.getenv('TIMEOUT_CLEANUP_LOCAL', '120'))
+    POD_ID = os.getenv('POD_ID')
+
+
+@workflow.defn(name='cleanup_files')
+class CleanupFiles:
+    """
+    Cleanup workflow for removing stale files.
+
+    This workflow cleans up:
+    - Local temporary directories with timestamp suffixes
+
+    The workflow is designed to be simple and robust, with error handling
+    delegated to the individual activities.
+    """
+
+    @workflow.run
+    async def run(self, input_data: dict[str, Any] | None = None) -> None:
+        """
+        Execute the cleanup workflow.
+
+        This method orchestrates the cleanup of local directories
+        in sequence. No exception handling is needed as activities handle their
+        own errors and notifications.
+        """
+        payload = input_data or {}
+        temp_path = payload.get('temp_path') or REPORTS_TEMP_DIR
+
+        # Metadata for tracking
+        metadata = {
+            'metadata': {
+                'pod_id': POD_ID,
+                'workflow_name': 'cleanup_files',
+            }
+        }
+
+        # Execute local directory cleanup
+        await workflow.execute_activity_method(
+            Activities.cleanup_temp_directories,
+            {
+                **metadata,
+                'temp_path': temp_path,
+            },
+            retry_policy=no_retry_policy,
+            start_to_close_timeout=timedelta(seconds=TIMEOUT_CLEANUP_LOCAL),
+        )

model_manager/workflows/train_model.py

@@ -0,0 +1,400 @@
+"""
+Train Model Workflow for ML model training pipeline.
+
+This workflow orchestrates the complete ML model training process, including:
+- Parameter validation and conversion
+- Data download from MinIO
+- Model training
+- Model saving to MLFlow
+- Experiment tracking and status updates
+"""
+
+from temporalio import workflow
+
+with workflow.unsafe.imports_passed_through():
+    import os
+    from datetime import timedelta
+    from typing import Any
+
+    from temporalio.common import RetryPolicy
+    from temporalio.exceptions import ApplicationError
+
+    from model_manager.activities.activities import Activities
+    from model_manager.activities.experiment_tracking import UpdateType
+    from model_manager.utils.models.experiment_status import ExperimentStatus
+
+    # Activity timeouts (seconds). Tune per environment (large uploads, long training).
+    # Training uses no_retry_policy: extend TIMEOUT_TRAIN_MODEL instead of adding retries
+    # to avoid duplicate MLflow side effects. Cleanup/delete uses network_retry_policy.
+    TIMEOUT_VALIDATE_PARAMS = int(os.getenv('TIMEOUT_VALIDATE_PARAMS', '30'))
+    TIMEOUT_TRAIN_MODEL = int(os.getenv('TIMEOUT_TRAIN_MODEL', '2700'))
+    TIMEOUT_DELETE_FILE = int(os.getenv('TIMEOUT_DELETE_FILE', '120'))
+    TIMEOUT_UPDATE_DATABASE = int(os.getenv('TIMEOUT_UPDATE_DATABASE', '30'))
+
+    # Retry Policies - Granular strategies for different operation types
+    # Fast retry for transient network errors (MinIO operations)
+    network_retry_policy = RetryPolicy(
+        initial_interval=timedelta(seconds=1),
+        maximum_interval=timedelta(seconds=10),
+        backoff_coefficient=2.0,
+        maximum_attempts=5,
+    )
+
+    # No retry for training - data errors are permanent
+    no_retry_policy = RetryPolicy(
+        maximum_attempts=1,
+    )
+
+    # Database retry with exponential backoff
+    database_retry_policy = RetryPolicy(
+        initial_interval=timedelta(seconds=2),
+        maximum_interval=timedelta(seconds=20),
+        backoff_coefficient=2.0,
+        maximum_attempts=5,
+    )
+
+
+@workflow.defn(name='train_model')
+class TrainModel:
+    """
+    Complete ML model training workflow.
+
+    This workflow implements the full training pipeline from parameter validation
+    through model training and saving to MLFlow. It provides comprehensive error
+    handling with database status updates at each stage.
+
+    The workflow ensures:
+    - Proper parameter validation before training starts
+    - Status tracking in database for monitoring
+    - Error handling with detailed error messages
+    - Cleanup and proper resource management
+    """
+
+    @workflow.run
+    async def run(self, input_data: dict[str, Any]) -> dict[str, Any] | None:
+        """
+        Execute the complete model training workflow.
+
+        This method orchestrates all steps of the training pipeline:
+        1. Validate and convert training parameters
+        2. Download training data from MinIO
+        3. Train the model
+        4. Save model to MLFlow
+        5. Update experiment tracking status
+
+        Args:
+            input_data: Complete configuration for the training workflow
+                All TrainModelParams fields at the same level:
+                    - experiment_run_id (int): Unique identifier for the experiment run (REQUIRED)
+                    - target_variable (str): Target variable to predict
+                    - variable_columns (list[str]): Feature columns
+                    - train_size (int): Training data percentage
+                    - ... (all other TrainModelParams fields)
+
+        Raises:
+            ValueError: If experiment_run_id is missing or invalid
+        """
+        workflow.logger.info(f'Starting train_model workflow for {input_data}')
+
+        try:
+            experiment_run_id = self._validate_experiment_run_id(input_data)
+        except ValueError as exc:
+            # Prevent workflow-task retries on deterministic input contract violations.
+            raise ApplicationError(str(exc), non_retryable=True) from exc
+        input_data = {**input_data, 'experiment_run_id': experiment_run_id}
+
+        model_name = input_data.get('model_name')
+        model_id = input_data.get('model_id')
+
+        metadata = {
+            'metadata': {
+                'experiment_run_id': experiment_run_id,
+                'workflow_name': 'train_model',
+                'model_name': model_name,
+                'model_id': model_id,
+            }
+        }
+
+        train_params = await self._validate_training_parameters(
+            input_data, experiment_run_id, metadata
+        )
+
+        training_succeeded = False
+        train_result: dict[str, Any] | None = None
+
+        try:
+            train_result = await self._train_model(
+                train_params=train_params,
+                experiment_run_id=experiment_run_id,
+                metadata=metadata,
+            )
+            training_succeeded = True
+        finally:
+            try:
+                if train_result is not None:
+                    await self._cleanup_resources(
+                        run_dir=train_result.get('run_dir'),
+                        metadata=metadata,
+                    )
+                else:
+                    pass
+            except Exception:  # noqa: BLE001
+                # If cleanup fails after training failed, there is nothing extra to log (DB not committed).
+                if training_succeeded:  # pragma: no branch
+                    workflow.logger.warning(
+                        'cleanup_resources failed after successful training; model and DB status '
+                        'are already committed. Temp files may remain until scheduled cleanup.',
+                    )
+        return train_result
+
+    def _validate_experiment_run_id(self, input_data: dict[str, Any]) -> int:
+        """
+        Validate experiment_run_id from input data.
+
+        This method ensures that experiment_run_id is present and valid.
+        Without a valid experiment_run_id, we cannot update database status,
+        so this validation must happen before any other operation.
+
+        Args:
+            input_data: Input data dictionary containing experiment_run_id
+
+        Returns:
+            int: Validated experiment_run_id
+
+        Raises:
+            ValueError: If experiment_run_id is missing or not an integer
+        """
+        experiment_run_id = input_data.get('experiment_run_id')
+
+        if experiment_run_id is None:
+            raise ValueError('experiment_run_id is required but was not provided')
+
+        if isinstance(experiment_run_id, int):
+            return experiment_run_id
+
+        if isinstance(experiment_run_id, str) and experiment_run_id.strip().isdigit():
+            return int(experiment_run_id.strip())
+
+        raise ValueError(
+            f'experiment_run_id must be an integer or numeric string, got {type(experiment_run_id).__name__}'
+        )
+
+    async def _validate_training_parameters(
+        self,
+        input_data: dict[str, Any],
+        experiment_run_id: int,
+        metadata: dict[str, Any],
+    ) -> dict[str, Any]:
+        """
+        Validate and convert training parameters from dict to TrainModelParams.
+
+        This method calls the validate_train_params activity to convert and validate
+        the input parameters. On success, updates DB status to ORCHESTRATOR_WAITING_PROC.
+        On error, updates DB status to ORCHESTRATOR_VALIDATION_ERROR.
+
+        Args:
+            input_data: Input data dictionary containing all training parameters
+            experiment_run_id: Validated experiment run ID
+            metadata: Workflow execution metadata
+
+        Returns:
+            dict[str, Any]: Validated training parameters
+
+        Raises:
+            Exception: If validation fails (after updating DB status)
+        """
+        try:
+            input_data = await workflow.execute_activity_method(
+                Activities.load_model_metadata,
+                {
+                    **input_data,
+                    **metadata,
+                },
+                retry_policy=no_retry_policy,
+                start_to_close_timeout=timedelta(seconds=TIMEOUT_VALIDATE_PARAMS),
+            )
+
+            train_params = await workflow.execute_activity_method(
+                Activities.validate_train_params,
+                {
+                    **input_data,
+                    **metadata,
+                },
+                retry_policy=no_retry_policy,
+                start_to_close_timeout=timedelta(seconds=TIMEOUT_VALIDATE_PARAMS),
+            )
+
+            await self._update_experiment_run(
+                metadata=metadata,
+                experiment_run_id=experiment_run_id,
+                update_type=UpdateType.STATUS,
+                status=ExperimentStatus.ORCHESTRATOR_WAITING_PROC,
+            )
+
+            return train_params
+        except Exception as e:
+            try:
+                await self._update_experiment_run(
+                    metadata=metadata,
+                    experiment_run_id=experiment_run_id,
+                    update_type=UpdateType.STATUS_WITH_ERROR,
+                    status=ExperimentStatus.ORCHESTRATOR_VALIDATION_ERROR,
+                    error_message=self._extract_error_message(e),
+                )
+            except Exception as secondary:  # noqa: BLE001
+                workflow.logger.warning(
+                    'Failed to persist ORCHESTRATOR_VALIDATION_ERROR to experiment_run: %s',
+                    secondary,
+                )
+            raise
+
+    async def _train_model(
+        self,
+        train_params: dict[str, Any],
+        experiment_run_id: int,
+        metadata: dict[str, Any],
+    ) -> dict[str, Any]:
+        """
+        Download file from MinIO and train model.
+
+        This method orchestrates the download and training steps using proper
+        resource management with try/catch/finally. On success, updates DB status
+        to TRAINING_SUCCESS. On error, updates DB status to TRAINING_ERROR.
+
+        Args:
+            train_params: TrainModelParams object with training configuration
+            experiment_run_id: Validated experiment run ID
+            metadata: Workflow execution metadata
+
+        Returns:
+            dict[str, Any]: Serializable training summary from the train_model activity
+
+        Raises:
+            Exception: If download or training fails (after updating DB status)
+        """
+        try:
+            train_result = await workflow.execute_activity_method(
+                Activities.train_model,
+                {
+                    **metadata,
+                    'train_params': train_params,
+                },
+                retry_policy=no_retry_policy,
+                start_to_close_timeout=timedelta(seconds=TIMEOUT_TRAIN_MODEL),
+            )
+
+            await self._update_experiment_run(
+                metadata=metadata,
+                experiment_run_id=experiment_run_id,
+                update_type=UpdateType.MODEL_SAVED,
+                status=ExperimentStatus.TRAINING_SUCCESS,
+                run_name=train_result.get('run_name'),
+            )
+
+            return train_result
+        except Exception as e:
+            try:
+                await self._update_experiment_run(
+                    metadata=metadata,
+                    experiment_run_id=experiment_run_id,
+                    update_type=UpdateType.STATUS_WITH_ERROR,
+                    status=ExperimentStatus.TRAINING_ERROR,
+                    error_message=self._extract_error_message(e),
+                )
+            except Exception as secondary:  # noqa: BLE001
+                workflow.logger.warning(
+                    'Failed to persist TRAINING_ERROR status to experiment_run: %s',
+                    secondary,
+                )
+            raise
+
+    async def _cleanup_resources(
+        self,
+        run_dir: str | None,
+        metadata: dict[str, Any],
+    ) -> None:
+        """
+        Cleanup resources.
+
+        This method removes the temporary run directory via activity.
+
+        Args:
+            run_dir: Temporary directory to remove
+            metadata: Workflow execution metadata
+        """
+        if run_dir is None:
+            return
+
+        await workflow.execute_activity_method(
+            Activities.cleanup_resources,
+            {
+                **metadata,
+                'run_dir': run_dir,
+            },
+            retry_policy=network_retry_policy,
+            start_to_close_timeout=timedelta(seconds=TIMEOUT_DELETE_FILE),
+        )
+
+    async def _update_experiment_run(
+        self,
+        metadata: dict[str, Any],
+        experiment_run_id: int,
+        update_type: UpdateType,
+        status: ExperimentStatus,
+        error_message: str | None = None,
+        run_name: str | None = None,
+    ) -> None:
+        """
+        Update experiment run status in the database.
+
+        This is a helper method to simplify calls to the update_experiment_run activity.
+        It handles both success and error status updates.
+
+        Args:
+            metadata: Workflow execution metadata
+            experiment_run_id: Unique identifier for the experiment run
+            update_type: Type of update (STATUS, STATUS_WITH_ERROR, or MODEL_SAVED)
+            status: Status to set in the database
+            error_message: Error message (required if update_type is STATUS_WITH_ERROR)
+            run_name: MLFlow run name (required if update_type is MODEL_SAVED)
+        """
+        update_input = {
+            **metadata,
+            'experiment_run_id': experiment_run_id,
+            'update_type': update_type,
+            'status': status,
+        }
+
+        if error_message is not None:
+            update_input['error_message'] = error_message
+
+        if run_name is not None:
+            update_input['run_name'] = run_name
+
+        await workflow.execute_activity_method(
+            Activities.update_experiment_run,
+            update_input,
+            retry_policy=database_retry_policy,
+            start_to_close_timeout=timedelta(seconds=TIMEOUT_UPDATE_DATABASE),
+        )
+
+    def _extract_error_message(self, exc: Exception) -> str:
+        message_parts: list[str] = []
+        seen: set[int] = set()
+        current: Exception | None = exc
+
+        while current and id(current) not in seen:
+            seen.add(id(current))
+            text = str(current).strip()
+
+            if text and text not in message_parts:
+                message_parts.append(text)
+
+            cause = getattr(current, '__cause__', None)
+            context = getattr(current, '__context__', None)
+            current = cause if isinstance(cause, Exception) else context
+
+        if not message_parts:
+            return repr(exc)
+
+        return ' | '.join(message_parts)

pyproject.toml

@@ -0,0 +1,203 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "model-manager"
+version = "1.2.0"
+description = "Sientia DataOps Model Manager - ML Model Orchestration System"
+readme = "README.md"
+requires-python = ">=3.11"
+authors = [
+    {name = "Aignosi", email = "dev@aignosi.com"}
+]
+
+[tool.ruff]
+line-length = 100
+target-version = "py311"
+exclude = [
+    ".git",
+    ".venv",
+    "venv",
+    "__pycache__",
+    "*.pyc",
+    ".pytest_cache",
+    "htmlcov",
+]
+
+[tool.ruff.lint]
+select = [
+    "E",   # pycodestyle errors
+    "W",   # pycodestyle warnings
+    "F",   # pyflakes
+    "I",   # isort
+    "B",   # flake8-bugbear
+    "C4",  # flake8-comprehensions
+    "UP",  # pyupgrade
+    "N",   # pep8-naming
+    "YTT", # flake8-2020
+    "S",   # flake8-bandit
+    "BLE", # flake8-blind-except
+    "A",   # flake8-builtins
+    "C90", # mccabe complexity
+]
+
+ignore = [
+    "E501",  # line too long (handled by formatter)
+    "S101",  # use of assert (needed for tests)
+    "S105",  # possible hardcoded password (false positives)
+    "S106",  # possible hardcoded password (false positives)
+    "N802",  # function name should be lowercase (temporal decorators)
+    "N806",  # variable in function should be lowercase
+]
+
+[tool.ruff.lint.per-file-ignores]
+"tests/**/*.py" = [
+    "S101",  # assert allowed in tests
+    "S105",  # hardcoded passwords ok in tests
+    "S106",  # hardcoded passwords ok in tests
+    "S108",  # temp paths are expected in tests
+]
+"e2e/**/*.py" = [
+    "S101",  # assert allowed in tests
+    "S105",  # hardcoded passwords ok in tests
+    "S106",  # hardcoded passwords ok in tests
+    "S108",  # temp paths are expected in tests
+    "ARG001", # unused function args in fixtures
+]
+
+[tool.ruff.lint.mccabe]
+max-complexity = 15
+
+[tool.ruff.format]
+quote-style = "single"
+indent-style = "space"
+line-ending = "auto"
+
+[tool.mypy]
+python_version = "3.11"
+warn_return_any = false
+warn_unused_configs = true
+disallow_untyped_defs = false
+disallow_incomplete_defs = false
+check_untyped_defs = true
+no_implicit_optional = true
+warn_redundant_casts = true
+warn_unused_ignores = false
+warn_no_return = true
+strict_equality = true
+ignore_missing_imports = false
+
+# Ignore missing imports for external packages
+[[tool.mypy.overrides]]
+module = "temporalio.*"
+ignore_missing_imports = true
+
+[[tool.mypy.overrides]]
+module = "sientia_do.*"
+ignore_missing_imports = true
+
+[[tool.mypy.overrides]]
+module = "mlflow.*"
+ignore_missing_imports = true
+
+[[tool.mypy.overrides]]
+module = "prometheus_client.*"
+ignore_missing_imports = true
+
+[[tool.mypy.overrides]]
+module = "pandas.*"
+ignore_missing_imports = true
+
+[[tool.mypy.overrides]]
+module = "bs4.*"
+ignore_missing_imports = true
+
+[[tool.mypy.overrides]]
+module = "evidently.*"
+ignore_missing_imports = true
+
+[[tool.mypy.overrides]]
+module = "sklearn.*"
+ignore_missing_imports = true
+
+[[tool.mypy.overrides]]
+module = "sientia_model.*"
+ignore_missing_imports = true
+
+[[tool.mypy.overrides]]
+module = "yaml"
+ignore_missing_imports = true
+
+[[tool.mypy.overrides]]
+module = [
+    "model_manager.utils.repository.model_repository",
+    "model_manager.utils.repository.data_manager_repository",
+]
+ignore_errors = true
+
+[tool.pytest.ini_options]
+testpaths = ["tests", "e2e"]
+python_files = ["test_*.py"]
+python_classes = ["Test*"]
+python_functions = ["test_*"]
+addopts = [
+    "-v",
+    "--strict-markers",
+    "--cov=model_manager",
+    "--cov-report=term-missing",
+    "--cov-report=html",
+    "--cov-report=xml",
+]
+markers = [
+    "asyncio: marks tests as async",
+    "integration: marks tests as integration tests",
+    "unit: marks tests as unit tests",
+]
+
+[tool.pyright]
+reportMissingTypeStubs = false
+
+[tool.coverage.run]
+source = ["model_manager"]
+omit = [
+    "*/tests/*",
+    "*/venv/*",
+    "*/__pycache__/*",
+    "*/site-packages/*",
+]
+branch = true
+
+[tool.coverage.report]
+precision = 2
+show_missing = true
+skip_covered = false
+exclude_lines = [
+    "pragma: no cover",
+    "def __repr__",
+    "def __str__",
+    "raise AssertionError",
+    "raise NotImplementedError",
+    "if __name__ == .__main__.:",
+    "if TYPE_CHECKING:",
+    "class .*\\bProtocol\\):",
+    "@(abc\\.)?abstractmethod",
+]
+
+[tool.coverage.html]
+directory = "htmlcov"
+
+[tool.bandit]
+exclude_dirs = ["tests", "venv", ".venv"]
+skips = ["B101", "B601"]  # Skip assert and shell injection in controlled environments
+
+[tool.deptry]
+known_first_party = [
+    "model_manager"
+]
+requirements_files = [
+    "requirements.txt"
+]
+requirements_files_dev = [
+    "requirements-dev.txt"
+]

requirements-dev.txt

@@ -0,0 +1,23 @@
+# Development and Testing Dependencies
+# These packages are only needed for development, testing, and code quality checks
+# Install with: pip install -r requirements-dev.txt
+
+# Code Quality & Linting
+ruff>=0.1.0              # Fast Python linter and formatter (replaces flake8, black, isort)
+mypy>=1.7.0              # Static type checker
+bandit>=1.7.5            # Security vulnerability scanner
+pandas-stubs>=2.0.0      # Type stubs for pandas
+types-psycopg2>=2.9.0    # Type stubs for psycopg2
+types-requests>=2.31.0   # Type stubs for requests
+
+# Testing
+pytest>=7.4.0                       # Testing framework
+pytest-cov>=4.1.0                   # Coverage plugin for pytest
+pytest-asyncio>=0.21.0              # Async test support
+testcontainers[postgres,minio,mongodb]>=4.0.0  # Real containers for E2E tests
+requests>=2.31.0                    # HTTP client for Gitea REST API seeding (E2E)
+
+# Development Tools
+ipython>=8.12.0          # Enhanced Python shell
+ipdb>=0.13.13            # IPython debugger
+ipykernel<=6.26.0          # IPython kernel ( other versions cause problems with interactive mode)

requirements-local.txt

@@ -0,0 +1,10 @@
+temporalio==1.23.0
+psycopg2-binary==2.9.11
+sqlalchemy==2.0.49
+boto3==1.42.70
+botocore==1.42.70
+git+ssh://git@github.com/Aignosi/sientia-dataops-library.git@1.12.0
+git+ssh://git@github.com/Aignosi/sientia-model-library.git@0.8.3
+prometheus-client==0.23.1
+beautifulsoup4==4.12.3
+evidently==0.6.7

requirements.txt

@@ -0,0 +1,11 @@
+temporalio==1.23.0
+psycopg2-binary==2.9.11
+sqlalchemy==2.0.50
+boto3==1.42.70
+botocore==1.42.70
+sientia_do>=1.11.0
+sientia_model>=0.8.1
+prometheus-client==0.23.1
+beautifulsoup4==4.12.3
+evidently==0.6.7
+jsonschema==4.26.0

run_local.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+
+# Exit on any error
+set -e
+
+if [ -f .env ]; then
+    set -a
+    source <(cat .env | grep -v '^#' | grep -v '^$')
+    set +a
+    echo "Environment variables loaded from .env"
+else
+    echo "Warning: .env file not found. Continuing without environment variables."
+fi
+
+python -m model_manager.worker.worker

scripts/inputs/linear_regression.json

@@ -0,0 +1,48 @@
+{
+  "experiment": {
+    "experiment_run_id": 1001,
+    "experiment_name": "test-experiment-name",
+    "run_name": "test-run-name",
+    "username": "vitor.santos@aignosi.com.br",
+    "status": "ORCHESTRATOR_WAITING_PROC"
+  },
+  "minio": {
+    "mc_alias": "suse",
+    "bucket_name": "model-training",
+    "file_name": "training-sample-dataset-1001.csv",
+    "local_csv": "input_dataset.csv"
+  },
+  "temporal": {
+    "task_queue": "train_model-basic-queue",
+    "workflow_name": "train_model",
+    "execution_timeout_minutes": 5,
+    "run_timeout_minutes": 5,
+    "task_timeout_minutes": 5
+  },
+  "payload": {
+    "experiment_run_id": 1001,
+    "variable_columns": ["Counter", "Rollout"],
+    "target_variable": "Square",
+    "line_separator": ",",
+    "decimal_separator": ".",
+    "train_size": 80,
+    "shuffle": true,
+    "random_state": 42,
+    "model_name": "smoke-linreg",
+    "model_type": "linear_regression",
+    "model_id": 1001,
+    "data_model_kwargs": {},
+    "model_kwargs": {},
+    "opt_params": {},
+    "date_column": "timestamp"
+  },
+  "db_only": {
+    "model_metadata": {
+      "schemas": {
+        "components": {
+          "schemas": {}
+        }
+      }
+    }
+  }
+}

scripts/inputs/sin-approx.json

@@ -0,0 +1,49 @@
+{
+    "experiment": {
+      "experiment_run_id": 2000,
+      "experiment_name": "experiment-sin-approx",
+      "run_name": "run-sin-approx",
+      "username": "vitor.santos@aignosi.com.br",
+      "status": "ORCHESTRATOR_WAITING_PROC"
+    },
+    "minio": {
+      "mc_alias": "open-suse",
+      "bucket_name": "model-training",
+      "file_name": "training-sin-approx-dataset-1001.csv",
+      "local_csv": "data-1780946658143-pivot.csv"
+    },
+    "temporal": {
+      "task_queue": "train_model-basic-queue",
+      "workflow_name": "train_model",
+      "execution_timeout_minutes": 5,
+      "run_timeout_minutes": 5,
+      "task_timeout_minutes": 5
+    },
+    "payload": {
+      "experiment_run_id": 2000,
+      "variable_columns": ["SourceTri"],
+      "target_variable": "TargetSin",
+      "line_separator": ",",
+      "decimal_separator": ".",
+      "train_size": 80,
+      "shuffle": true,
+      "random_state": 42,
+      "model_name": "sin-approx",
+      "model_type": "linear_regression",
+      "model_id": 1,
+      "data_model_kwargs": {},
+      "model_kwargs": {},
+      "opt_params": {},
+      "date_column": "timestamp"
+    },
+    "db_only": {
+      "model_metadata": {
+        "schemas": {
+          "components": {
+            "schemas": {}
+          }
+        }
+      }
+    }
+  }
+  

scripts/inputs/xgboost.json

@@ -0,0 +1,63 @@
+{
+  "experiment": {
+    "experiment_run_id": 1002,
+    "experiment_name": "test-experiment-xgboost",
+    "run_name": "test-run-xgboost",
+    "username": "vitor.santos@aignosi.com.br",
+    "status": "ORCHESTRATOR_WAITING_PROC"
+  },
+  "minio": {
+    "mc_alias": "suse",
+    "bucket_name": "model-training",
+    "file_name": "training-sample-dataset-1002.csv",
+    "local_csv": "input_dataset.csv"
+  },
+  "temporal": {
+    "task_queue": "train_model-basic-queue",
+    "workflow_name": "train_model",
+    "execution_timeout_minutes": 5,
+    "run_timeout_minutes": 5,
+    "task_timeout_minutes": 5
+  },
+  "payload": {
+    "experiment_run_id": 1002,
+    "variable_columns": ["Counter", "Rollout"],
+    "target_variable": "Square",
+    "line_separator": ",",
+    "decimal_separator": ".",
+    "train_size": 80,
+    "shuffle": true,
+    "random_state": 42,
+    "model_name": "smoke-xgb",
+    "model_type": "xgboost",
+    "model_id": 1002,
+    "data_model_kwargs": {
+      "scaler_method": "MinMax",
+      "window_size": 3,
+      "use_filtering": false,
+      "transform_mode": "all"
+    },
+    "model_kwargs": {},
+    "opt_params": {
+      "tree_method": "hist",
+      "device": "cuda",
+      "learning_rate": 0.3,
+      "n_estimators": 100,
+      "max_depth": 32,
+      "subsample": 0.8,
+      "colsample_bytree": 0.8,
+      "min_child_weight": 5,
+      "random_state": 42
+    },
+    "date_column": "timestamp"
+  },
+  "db_only": {
+    "model_metadata": {
+      "schemas": {
+        "components": {
+          "schemas": {}
+        }
+      }
+    }
+  }
+}

scripts/run_cleanup_test.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+"""Run cleanup_files workflow once for manual testing.
+
+This script starts the Temporal workflow `cleanup_files` a single time,
+using the same Temporal namespace and task queue as the main worker.
+
+It is intended only for local/manual testing; scheduling (cron) must be
+configured separately in Temporal.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import os
+import sys
+from datetime import timedelta
+from pathlib import Path
+from typing import Any
+
+from dotenv import load_dotenv
+from temporalio.client import Client
+
+# Ensure project root is on PYTHONPATH when running directly (must run before model_manager import)
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if ROOT_DIR not in sys.path:
+    sys.path.insert(0, ROOT_DIR)
+
+from model_manager.workflows.cleanup_files import CleanupFiles  # noqa: E402
+
+# Carrega variáveis de ambiente do arquivo .env na raiz do projeto
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+ENV_PATH = PROJECT_ROOT / '.env'
+if ENV_PATH.exists():
+    load_dotenv(dotenv_path=ENV_PATH)
+
+
+async def main(argv: list[str]) -> None:
+    """Entry point for manual cleanup workflow execution.
+
+    Args:
+        argv: Command-line arguments (excluding program name).
+    """
+
+    # Config from environment / defaults
+    temporal_host = os.getenv('TEMPORAL_HOST')
+    temporal_namespace = os.getenv('TEMPORAL_NAMESPACE')
+    task_queue = os.getenv('CLEANUP_TASK_QUEUE')
+    use_tls = os.getenv('TEMPORAL_USE_TLS', 'false').lower() == 'true'
+
+    print(f'Connecting to Temporal at {temporal_host} (namespace={temporal_namespace})...')
+    client = await Client.connect(
+        target_host=temporal_host,
+        namespace=temporal_namespace,
+        tls=use_tls,
+    )
+
+    input_data: dict[str, Any] = {
+    }
+
+    workflow_id = f'cleanup-files-manual-{int(asyncio.get_event_loop().time())}'
+
+    print(
+        f'Starting cleanup_files workflow once...\n'
+        f'  workflow_id = {workflow_id}\n'
+        f'  task_queue  = {task_queue}'
+    )
+
+    handle = await client.start_workflow(
+        CleanupFiles.run,
+        input_data,
+        id=workflow_id,
+        task_queue=task_queue,
+        run_timeout=timedelta(minutes=10),
+    )
+
+    print('Workflow started, waiting for completion...')
+    await handle.result()
+    print('cleanup_files workflow completed successfully.')
+
+
+if __name__ == '__main__':  # pragma: no cover - manual utility script
+    asyncio.run(main(sys.argv[1:]))

scripts/run_training_test.py

@@ -0,0 +1,310 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#   kernelspec:
+#     display_name: Python 3
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # Training smoke test (`input-sample.md`)
+#
+# Run cells top to bottom in VS Code / Cursor (**Run Cell** on each `# %%` block).
+#
+# Steps mirror `input-sample.md`: optional DB delete + insert, `mc cp` to MinIO, Temporal `train_model`.
+# Set `POSTGRES_*`, `TEMPORAL_*`, and configure the `mc` alias. Add/remove entries in
+# `TRAINING_TEST_INPUTS` below to choose which experiments run.
+
+# %%
+from __future__ import annotations
+
+import csv
+import json
+import os
+import subprocess
+import uuid
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Any
+
+import psycopg2
+from dotenv import load_dotenv
+from psycopg2.extras import Json
+from temporalio import client
+
+TRAINING_TEST_INPUTS: list[str] = [
+    'scripts/inputs/linear_regression.json',
+    'scripts/inputs/xgboost.json',
+]
+
+TRAINING_TEST_INPUTS: list[str] = [
+    'scripts/inputs/sin-approx.json',
+]
+
+_REQUIRED_INPUT_KEYS = ('experiment', 'minio', 'temporal', 'payload', 'db_only')
+
+
+def _load_input(path: Path) -> dict[str, Any]:
+    """
+    Load a training smoke-test input JSON and validate required top-level keys.
+
+    Args:
+     - path: Path to the input JSON file
+
+    Return:
+     Parsed input dict with experiment, minio, temporal, payload, and db_only sections
+    """
+    data: dict[str, Any] = json.loads(path.read_text(encoding='utf-8'))
+    missing = [key for key in _REQUIRED_INPUT_KEYS if key not in data]
+    if missing:
+        raise KeyError(f'Input JSON missing required top-level keys: {", ".join(missing)}')
+    return data
+
+
+def _postgres_connect_kwargs() -> dict[str, str | int]:
+    """
+    Build psycopg2.connect keyword arguments from POSTGRES_* environment variables.
+
+    Return:
+     host, port, user, password, and dbname suitable for psycopg2.connect
+    """
+    host = os.getenv('POSTGRES_HOST')
+    user = os.getenv('POSTGRES_USER')
+    password = os.getenv('POSTGRES_PASSWORD')
+    dbname = os.getenv('POSTGRES_DBNAME')
+    if not host or not user or not password or not dbname:
+        raise RuntimeError(
+            'Set POSTGRES_HOST, POSTGRES_USER, POSTGRES_PASSWORD, and POSTGRES_DBNAME'
+        )
+    return {
+        'host': host,
+        'port': int(os.getenv('POSTGRES_PORT', '5432')),
+        'user': user,
+        'password': password,
+        'dbname': dbname,
+    }
+
+
+def _resolve_input_paths(project_root: Path, entries: list[str]) -> list[Path]:
+    """
+    Resolve and validate the hardcoded TRAINING_TEST_INPUTS list against the filesystem.
+
+    Args:
+     - project_root: Repository root used to resolve relative entries
+     - entries: Input file paths (absolute or relative to project_root)
+
+    Return:
+     Absolute paths to the input JSON files, in declaration order
+    """
+    if not entries:
+        raise RuntimeError('TRAINING_TEST_INPUTS is empty; add at least one input JSON path')
+    resolved: list[Path] = []
+    for entry in entries:
+        candidate = Path(entry)
+        if not candidate.is_absolute():
+            candidate = project_root / candidate
+        if not candidate.is_file():
+            raise FileNotFoundError(f'Training test input file not found: {candidate}')
+        resolved.append(candidate.resolve())
+    return resolved
+
+
+def _validate_csv_columns(
+    local_csv: Path,
+    payload: dict[str, Any],
+    input_path: Path,
+) -> None:
+    """
+    Fail fast when the local CSV header does not match payload column names.
+
+    Args:
+     - local_csv: Resolved path to the CSV on disk
+     - payload: Training payload with variable_columns, target_variable, and separators
+     - input_path: Input JSON path (for error messages)
+
+    Return:
+     None; raises ValueError when required columns are missing from the CSV header
+    """
+    line_separator = str(payload.get('line_separator', ','))
+    with local_csv.open(newline='', encoding='utf-8') as handle:
+        header = next(csv.reader(handle, delimiter=line_separator))
+    required = list(payload['variable_columns']) + [str(payload['target_variable'])]
+    date_column = payload.get('date_column')
+    if date_column:
+        required.append(str(date_column))
+    missing = [column for column in required if column not in header]
+    if missing:
+        raise ValueError(
+            f'{input_path}: CSV {local_csv} header {header!r} is missing columns '
+            f'{missing!r} (check payload variable_columns, target_variable, date_column)'
+        )
+
+
+def _resolve_local_csv(project_root: Path, local_csv: str) -> Path:
+    """
+    Resolve a minio.local_csv entry against the project root when relative.
+
+    Args:
+     - project_root: Repository root
+     - local_csv: Path declared in the input JSON (absolute or relative)
+
+    Return:
+     Absolute path to the local CSV file
+    """
+    candidate = Path(local_csv)
+    if not candidate.is_absolute():
+        candidate = project_root / candidate
+    return candidate
+
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+load_dotenv(PROJECT_ROOT / '.env')
+
+experiments: list[dict[str, Any]] = []
+for _input_path in _resolve_input_paths(PROJECT_ROOT, TRAINING_TEST_INPUTS):
+    _input = _load_input(_input_path)
+    _local_csv = _resolve_local_csv(PROJECT_ROOT, _input['minio']['local_csv'])
+    _validate_csv_columns(_local_csv, _input['payload'], _input_path)
+    experiments.append({'path': _input_path, **_input})
+    _payload = _input['payload']
+    _experiment = _input['experiment']
+    print(
+        f'input={_input_path} '
+        f'model_type={_payload["model_type"]} '
+        f'model_name={_payload["model_name"]} '
+        f'experiment_run_id={_experiment["experiment_run_id"]}'
+    )
+
+# %%
+# --- configuration (`.env` at repo root; per-run values from input JSON) ---
+
+PG = _postgres_connect_kwargs()
+
+TEMPORAL_HOST = os.getenv('TEMPORAL_HOST')
+TEMPORAL_NAMESPACE = os.getenv('TEMPORAL_NAMESPACE')
+TEMPORAL_TLS = os.getenv('TEMPORAL_USE_TLS', 'false').lower() in ('1', 'true', 'yes')
+
+print(PG)
+print(TEMPORAL_HOST, TEMPORAL_NAMESPACE, TEMPORAL_TLS)
+for exp in experiments:
+    exp_minio = exp['minio']
+    exp_temporal = exp['temporal']
+    print(
+        exp_minio['mc_alias'],
+        exp_minio['bucket_name'],
+        exp_minio['file_name'],
+        _resolve_local_csv(PROJECT_ROOT, exp_minio['local_csv']),
+        exp_temporal['task_queue'],
+    )
+
+# %%
+# --- 1) database: delete previous row (same id), then insert `experiment_run` for each experiment ---
+# Primary key column is `id` (see `experiment_tracking` updates).
+
+now = datetime.utcnow()
+
+with psycopg2.connect(
+    host=PG['host'],
+    port=PG['port'],
+    user=PG['user'],
+    password=PG['password'],
+    dbname=PG['dbname'],
+) as conn:
+    with conn.cursor() as cur:
+        for exp in experiments:
+            exp_experiment = exp['experiment']
+            exp_minio = exp['minio']
+            exp_payload = exp['payload']
+            exp_db_only = exp['db_only']
+            request_data = {
+                **exp_payload,
+                'bucket_name': exp_minio['bucket_name'],
+                'file_name': exp_minio['file_name'],
+                'model_metadata': exp_db_only['model_metadata'],
+            }
+            cur.execute(
+                'DELETE FROM public.experiment_run WHERE id = %s',
+                (exp_experiment['experiment_run_id'],),
+            )
+            cur.execute(
+                """
+                INSERT INTO public.experiment_run (
+                    id, experiment_name, run_name, username, status, error_message,
+                    created_at, updated_at, bucket_name, file_name, request_data, orchestrator_response_data
+                )
+                VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
+                """,
+                (
+                    exp_experiment['experiment_run_id'],
+                    exp_experiment['experiment_name'],
+                    exp_experiment['run_name'],
+                    exp_experiment['username'],
+                    exp_experiment['status'],
+                    None,
+                    now,
+                    now,
+                    exp_minio['bucket_name'],
+                    exp_minio['file_name'],
+                    Json(request_data),
+                    None,
+                ),
+            )
+
+# %%
+# --- 2) MinIO: upload local CSV for each experiment (requires `mc` CLI and alias configured) ---
+for exp in experiments:
+    exp_minio = exp['minio']
+    local_csv = _resolve_local_csv(PROJECT_ROOT, exp_minio['local_csv'])
+    subprocess.run(
+        [
+            'mc',
+            'cp',
+            '--insecure',
+            str(local_csv),
+            f'{exp_minio["mc_alias"]}/{exp_minio["bucket_name"]}/{exp_minio["file_name"]}',
+        ],
+        check=True,
+    )
+
+# %%
+# --- 3) Temporal: connect once, then start `train_model` per experiment ---
+
+if not TEMPORAL_HOST or not TEMPORAL_NAMESPACE:
+    raise RuntimeError('Set TEMPORAL_HOST and TEMPORAL_NAMESPACE in the environment')
+
+c = await client.Client.connect(  # type: ignore[top-level-await]
+    target_host=TEMPORAL_HOST,
+    namespace=TEMPORAL_NAMESPACE,
+    tls=TEMPORAL_TLS,
+)
+
+# %%
+
+for exp in experiments:
+    exp_minio = exp['minio']
+    exp_temporal = exp['temporal']
+    exp_payload = exp['payload']
+    workflow_input = {
+        **exp_payload,
+        'bucket_name': exp_minio['bucket_name'],
+        'file_name': exp_minio['file_name'],
+    }
+    wid = f'train-model-test-{uuid.uuid4()}'
+    result = await c.execute_workflow(  # type: ignore[top-level-await, call-overload]
+        exp_temporal['workflow_name'],
+        workflow_input,
+        id=wid,
+        task_queue=exp_temporal['task_queue'],
+        execution_timeout=timedelta(minutes=exp_temporal['execution_timeout_minutes']),
+        run_timeout=timedelta(minutes=exp_temporal['run_timeout_minutes']),
+        task_timeout=timedelta(minutes=exp_temporal['task_timeout_minutes']),
+    )
+    print(wid)
+    print(result)
+
+# %%

sonar-project.properties

@@ -0,0 +1,9 @@
+sonar.projectKey=Aignosi_sientia-dataops-model-manager_dc6e051c-995a-4b57-9cde-f511018184c7
+sonar.projectName=sientia-dataops-model-manager
+sonar.sources=model_manager
+sonar.tests=tests
+sonar.qualitygate.wait=true
+sonar.qualitygate.timeout=300
+sonar.python.coverage.reportPaths=coverage.xml
+sonar.python.xunit.reportPath=pytest.xml
+sonar.python.version=3.11

t.py

@@ -0,0 +1,24 @@
+# %%
+from pandas import read_csv, to_datetime
+
+df = read_csv('data-1780946658143.csv')
+
+# %%
+df.head()
+
+# %%
+# normalize timestamp to naive "yyyy-MM-dd HH:mm:ss" (drop the "+00" UTC offset)
+df['timestamp'] = to_datetime(df['timestamp'], utc=True).dt.tz_localize(None)
+df['timestamp'] = df['timestamp'].dt.strftime('%Y-%m-%d %H:%M:%S')
+
+# %%
+# pivot the dataframe
+df = df.pivot(index='timestamp', columns='variable', values='value')
+
+# %%
+df.head()
+
+
+# %%
+df.to_csv('data-1780946658143-pivot.csv')
+# %%

tests/activities/test_activities.py

@@ -0,0 +1,144 @@
+"""Unit tests for Activities orchestrator (constructor, shutdown, destructor)."""
+
+from unittest.mock import MagicMock, Mock, patch
+
+import pytest
+from sientia_do.observability.sientia_monitoring import SientiaMonitoring
+
+from model_manager.activities.activities import Activities
+
+
+def _postgres():
+    return {
+        'host': 'h',
+        'port': 5432,
+        'user': 'u',
+        'password': 'p',
+        'dbname': 'db',
+        'min_connections': 1,
+        'max_connections': 2,
+    }
+
+
+def _mlflow():
+    return {'url': 'http://mlflow:5000', 'username': 'u', 'password': 'p'}
+
+
+def _minio(endpoint_url: str):
+    return {
+        'endpoint_url': endpoint_url,
+        'access_key': 'a',
+        'secret_key': 's',
+        'region': 'r',
+        'use_ssl': True,
+        'default_bucket': 'test-bucket',
+    }
+
+
+@pytest.mark.parametrize(
+    'endpoint,expected_endpoint',
+    [
+        ('http://minio:9000', 'minio:9000'),
+        ('https://minio:9000', 'minio:9000'),
+        ('minio:9000', 'minio:9000'),
+    ],
+)
+def test_activities_strips_minio_endpoint_scheme(endpoint, expected_endpoint):
+    with (
+        patch(
+            'model_manager.activities.activities.ExperimentTracking.__init__',
+            Mock(return_value=None),
+        ),
+        patch('model_manager.activities.activities.Training.__init__', Mock(return_value=None)),
+        patch('model_manager.activities.activities.Cleanup.__init__', Mock(return_value=None)),
+        patch('model_manager.activities.activities.SientiaMLflowRepository') as m_mlflow,
+        patch('model_manager.activities.activities.MinioRepository') as m_minio,
+    ):
+        Activities(
+            postgres_config=_postgres(),
+            mlflow_config=_mlflow(),
+            minio_config=_minio(endpoint),
+            plugin_store=MagicMock(),
+            logger=MagicMock(),
+            notification_handler=MagicMock(),
+            metrics_controller=MagicMock(),
+        )
+    m_minio.assert_called_once()
+    assert m_minio.call_args.kwargs['endpoint'] == expected_endpoint
+    assert m_minio.call_args.kwargs['bucket'] == 'test-bucket'
+    m_mlflow.assert_called_once()
+
+
+def test_activities_shutdown_calls_parents():
+    with (
+        patch(
+            'model_manager.activities.activities.ExperimentTracking.__init__',
+            Mock(return_value=None),
+        ),
+        patch('model_manager.activities.activities.Training.__init__', Mock(return_value=None)),
+        patch('model_manager.activities.activities.Cleanup.__init__', Mock(return_value=None)),
+        patch('model_manager.activities.activities.SientiaMLflowRepository'),
+        patch('model_manager.activities.activities.MinioRepository'),
+        patch('model_manager.activities.activities.ExperimentTracking.close') as m_close,
+        patch('model_manager.activities.activities.SientiaMonitoring.shutdown') as m_mon,
+    ):
+        a = Activities(
+            postgres_config=_postgres(),
+            mlflow_config=_mlflow(),
+            minio_config=_minio('http://x:9000'),
+            plugin_store=MagicMock(),
+            logger=MagicMock(),
+            notification_handler=MagicMock(),
+            metrics_controller=MagicMock(),
+        )
+        with patch.object(SientiaMonitoring, 'info', Mock()):
+            a.shutdown()
+    m_close.assert_called_once()
+    m_mon.assert_called_once()
+
+
+def test_activities_del_with_engine_runs_without_error():
+    with (
+        patch(
+            'model_manager.activities.activities.ExperimentTracking.__init__',
+            Mock(return_value=None),
+        ),
+        patch('model_manager.activities.activities.Training.__init__', Mock(return_value=None)),
+        patch('model_manager.activities.activities.Cleanup.__init__', Mock(return_value=None)),
+        patch('model_manager.activities.activities.SientiaMLflowRepository'),
+        patch('model_manager.activities.activities.MinioRepository'),
+    ):
+        a = Activities(
+            postgres_config=_postgres(),
+            mlflow_config=_mlflow(),
+            minio_config=_minio('http://x:9000'),
+            plugin_store=MagicMock(),
+            logger=MagicMock(),
+            notification_handler=MagicMock(),
+            metrics_controller=MagicMock(),
+        )
+    a.engine = MagicMock()
+    Activities.__del__(a)
+
+
+def test_activities_del_without_engine_runs_without_error():
+    with (
+        patch(
+            'model_manager.activities.activities.ExperimentTracking.__init__',
+            Mock(return_value=None),
+        ),
+        patch('model_manager.activities.activities.Training.__init__', Mock(return_value=None)),
+        patch('model_manager.activities.activities.Cleanup.__init__', Mock(return_value=None)),
+        patch('model_manager.activities.activities.SientiaMLflowRepository'),
+        patch('model_manager.activities.activities.MinioRepository'),
+    ):
+        a = Activities(
+            postgres_config=_postgres(),
+            mlflow_config=_mlflow(),
+            minio_config=_minio('http://x:9000'),
+            plugin_store=MagicMock(),
+            logger=MagicMock(),
+            notification_handler=MagicMock(),
+            metrics_controller=MagicMock(),
+        )
+    Activities.__del__(a)

tests/activities/test_cleanup.py

@@ -0,0 +1,310 @@
+"""Unit tests for the Cleanup activity, ensuring 100% code coverage."""
+
+import os
+import shutil
+import tempfile
+from datetime import datetime, timedelta
+from importlib import reload
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+# Define mocks at the top level to be accessible by all tests
+
+
+@pytest.fixture
+def mock_logger():
+    """Fixture for a mock logger."""
+    return MagicMock()
+
+
+@pytest.fixture
+def mock_notification_handler():
+    """Fixture for a mock notification handler."""
+    return MagicMock()
+
+
+@pytest.fixture
+def mock_metrics_controller():
+    """Fixture for a mock metrics controller with async methods."""
+    controller = MagicMock()
+    controller.shutdown = AsyncMock()
+    controller.emit = AsyncMock()
+    return controller
+
+
+@pytest.fixture
+def temp_dir():
+    """Fixture to create and clean up a temporary directory."""
+    path = tempfile.mkdtemp()
+    yield path
+    shutil.rmtree(path)
+
+
+# --- Initialization Tests ---
+
+
+@patch.dict(
+    'model_manager.activities.cleanup.os.environ',
+    {
+        'CLEANUP_RETENTION_HOURS': '24',
+        'CLEANUP_DRY_RUN': 'false',
+    },
+)
+def test_cleanup_init_default_values(
+    mock_logger,
+    mock_notification_handler,
+    mock_metrics_controller,
+):
+    """Test Cleanup initialization uses default environment values."""
+    import model_manager.activities.cleanup
+
+    reload(model_manager.activities.cleanup)
+    from model_manager.activities.cleanup import Cleanup
+
+    cleanup = Cleanup(
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    assert cleanup.retention_hours == 24
+    assert cleanup.dry_run is False
+
+
+def test_cleanup_init_custom_env_values(
+    mock_logger,
+    mock_notification_handler,
+    mock_metrics_controller,
+):
+    """Test Cleanup initialization with custom environment values."""
+    with patch.dict(
+        os.environ,
+        {
+            'CLEANUP_RETENTION_HOURS': '48',
+            'CLEANUP_DRY_RUN': 'true',
+        },
+    ):
+        import model_manager.activities.cleanup
+
+        reload(model_manager.activities.cleanup)
+        from model_manager.activities.cleanup import Cleanup
+
+        cleanup = Cleanup(
+            logger=mock_logger,
+            notification_handler=mock_notification_handler,
+            metrics_controller=mock_metrics_controller,
+        )
+
+        assert cleanup.retention_hours == 48
+        assert cleanup.dry_run is True
+
+
+@patch.dict(os.environ, {'CLEANUP_RETENTION_HOURS': 'invalid'})
+def test_cleanup_init_invalid_env_value_raises_error():
+    """Test Cleanup module raises ValueError for invalid environment variables on import."""
+    import model_manager.activities.cleanup
+
+    with pytest.raises(ValueError):
+        reload(model_manager.activities.cleanup)
+
+
+# --- Temp Directory Cleanup Tests ---
+
+
+def test_cleanup_temp_directories_nonexistent_path(
+    mock_logger,
+    mock_notification_handler,
+    mock_metrics_controller,
+):
+    """Test temp directory cleanup with a non-existent path."""
+    from model_manager.activities.cleanup import Cleanup
+
+    cleanup = Cleanup(
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+    cleanup.warning = MagicMock()
+
+    cleanup.cleanup_temp_directories({'temp_path': '/nonexistent/path', 'metadata': {}})
+
+    cleanup.warning.assert_called_once()
+
+
+@patch.dict('model_manager.activities.cleanup.os.environ', {'CLEANUP_DRY_RUN': 'false'})
+def test_cleanup_temp_directories_success_with_deletions(
+    temp_dir,
+    mock_logger,
+    mock_notification_handler,
+    mock_metrics_controller,
+):
+    """Test successful deletion of old temporary directories."""
+    import model_manager.activities.cleanup
+
+    reload(model_manager.activities.cleanup)
+    from model_manager.activities.cleanup import Cleanup
+
+    cleanup = Cleanup(
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+    old_time = (datetime.now() - timedelta(hours=48)).strftime('%Y%m%d_%H%M%S_000000')
+    old_dir = os.path.join(temp_dir, f'old_dir_{old_time}')
+    os.makedirs(old_dir)
+
+    recent_time = (datetime.now() - timedelta(hours=1)).strftime('%Y%m%d_%H%M%S_000000')
+    recent_dir = os.path.join(temp_dir, f'recent_dir_{recent_time}')
+    os.makedirs(recent_dir)
+
+    cleanup.cleanup_temp_directories({'temp_path': temp_dir, 'metadata': {}})
+
+    assert not os.path.exists(old_dir)
+    assert os.path.exists(recent_dir)
+
+
+@patch.dict(os.environ, {'CLEANUP_DRY_RUN': 'true'})
+def test_cleanup_temp_directories_dry_run(
+    temp_dir,
+    mock_logger,
+    mock_notification_handler,
+    mock_metrics_controller,
+):
+    """Test temp directory cleanup in dry_run mode does not delete."""
+    import model_manager.activities.cleanup
+
+    reload(model_manager.activities.cleanup)
+    from model_manager.activities.cleanup import Cleanup
+
+    cleanup = Cleanup(
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+    old_time = (datetime.now() - timedelta(hours=48)).strftime('%Y%m%d_%H%M%S_000000')
+    old_dir = os.path.join(temp_dir, f'old_dir_{old_time}')
+    os.makedirs(old_dir)
+
+    cleanup.cleanup_temp_directories({'temp_path': temp_dir, 'metadata': {}})
+
+    assert os.path.exists(old_dir)
+
+
+@patch.dict('model_manager.activities.cleanup.os.environ', {'CLEANUP_DRY_RUN': 'false'})
+def test_cleanup_temp_directories_delete_error(
+    temp_dir,
+    mock_logger,
+    mock_notification_handler,
+    mock_metrics_controller,
+):
+    """Test graceful handling of errors during directory deletion."""
+    import model_manager.activities.cleanup
+
+    reload(model_manager.activities.cleanup)
+    from model_manager.activities.cleanup import Cleanup
+
+    cleanup = Cleanup(
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+    cleanup.error = MagicMock()
+
+    old_time = (datetime.now() - timedelta(hours=48)).strftime('%Y%m%d_%H%M%S_000000')
+    old_dir = os.path.join(temp_dir, f'old_dir_{old_time}')
+    os.makedirs(old_dir)
+
+    with patch('shutil.rmtree', side_effect=OSError('Permission Denied')):
+        cleanup.cleanup_temp_directories({'temp_path': temp_dir, 'metadata': {}})
+
+    cleanup.error.assert_called_once()
+
+
+# --- Utility Tests ---
+
+
+def test_cleanup_temp_directories_with_files_and_unmatched_dirs(
+    temp_dir,
+    mock_logger,
+    mock_notification_handler,
+    mock_metrics_controller,
+):
+    """Test that files and directories with non-matching names are skipped."""
+    import model_manager.activities.cleanup
+
+    reload(model_manager.activities.cleanup)
+    from model_manager.activities.cleanup import Cleanup
+
+    cleanup = Cleanup(
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+    cleanup.debug = MagicMock()
+
+    # Create a file and a directory with a non-matching name
+    with open(os.path.join(temp_dir, 'a_file.txt'), 'w') as f:
+        f.write('hello')
+    os.makedirs(os.path.join(temp_dir, 'a_directory_with_no_timestamp'))
+
+    cleanup.cleanup_temp_directories({'temp_path': temp_dir, 'metadata': {}})
+
+    # Ensure the debug message for skipping was called for the unmatched directory
+    cleanup.debug.assert_called_with(
+        'Skipping directory without timestamp pattern: a_directory_with_no_timestamp', {}
+    )
+
+
+def test_cleanup_temp_directories_invalid_timestamp_format(
+    temp_dir,
+    mock_logger,
+    mock_notification_handler,
+    mock_metrics_controller,
+):
+    """Test that a directory with an invalid timestamp format is handled correctly."""
+    import model_manager.activities.cleanup
+
+    reload(model_manager.activities.cleanup)
+    from model_manager.activities.cleanup import Cleanup
+
+    cleanup = Cleanup(
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+    cleanup.error = MagicMock()
+
+    # Create a directory with a malformed timestamp that matches the regex but fails parsing
+    malformed_dir_name = 'dir_20239999_999999_999999'
+    os.makedirs(os.path.join(temp_dir, malformed_dir_name))
+
+    cleanup.cleanup_temp_directories({'temp_path': temp_dir, 'metadata': {}})
+
+    cleanup.error.assert_called_once()
+
+
+def test_cleanup_temp_directories_generic_exception(
+    temp_dir,
+    mock_logger,
+    mock_notification_handler,
+    mock_metrics_controller,
+):
+    """Test that a generic exception during directory cleanup is handled."""
+    import model_manager.activities.cleanup
+
+    reload(model_manager.activities.cleanup)
+    from model_manager.activities.cleanup import Cleanup
+
+    cleanup = Cleanup(
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+    cleanup.send_notification = MagicMock()
+
+    with patch('os.listdir', side_effect=Exception('Unexpected OS Error')):
+        with pytest.raises(Exception, match='Unexpected OS Error'):
+            cleanup.cleanup_temp_directories({'temp_path': temp_dir, 'metadata': {}})
+
+    cleanup.send_notification.assert_called_once()

tests/activities/test_experiment_tracking.py

@@ -0,0 +1,643 @@
+"""Unit tests for ExperimentTracking class with 100% coverage."""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+
+@pytest.fixture
+def mock_logger():
+    """Create a mock logger."""
+    return MagicMock()
+
+
+@pytest.fixture
+def mock_notification_handler():
+    """Create a mock notification handler."""
+    return MagicMock()
+
+
+@pytest.fixture
+def mock_metrics_controller():
+    """Create a mock metrics controller."""
+    controller = MagicMock()
+    controller.shutdown = AsyncMock()
+    controller.emit = AsyncMock()
+    return controller
+
+
+@pytest.fixture
+def db_config():
+    """Create a valid database configuration."""
+    return {
+        'host': 'localhost',
+        'port': 5432,
+        'user': 'testuser',
+        'password': 'testpass',
+        'dbname': 'testdb',
+        'min_connections': 1,
+        'max_connections': 10,
+    }
+
+
+def test_experiment_tracking_init(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test ExperimentTracking initialization."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking
+
+    ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+
+def test_experiment_tracking_del_without_engine(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test __del__ when engine attribute does not exist."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    if hasattr(et, 'engine'):
+        delattr(et, 'engine')
+
+    et.__del__()
+
+
+def test_experiment_tracking_del_with_engine(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test __del__ when engine exists."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    et.engine = MagicMock()
+
+    class MockSuper:
+        def __del__(self):
+            pass
+
+    with patch('builtins.super', return_value=MockSuper()):
+        et.__del__()
+
+
+def test_experiment_tracking_del_with_engine_exception(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test __del__ catches exceptions."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    et.engine = MagicMock()
+
+    class MockSuperWithError:
+        _should_raise: bool
+
+        def __init__(self) -> None:
+            self._should_raise = False
+
+        def __del__(self):
+            # Only raise error if not being cleaned up by garbage collector
+            # This prevents the PytestUnraisableExceptionWarning
+            if hasattr(self, '_should_raise') and self._should_raise:
+                raise RuntimeError('Test error')
+
+    # Suppress the PytestUnraisableExceptionWarning for this specific test
+    import warnings
+
+    warnings.filterwarnings('ignore', category=pytest.PytestUnraisableExceptionWarning)
+
+    mock_super = MockSuperWithError()
+    mock_super._should_raise = True
+    try:
+        with patch('builtins.super', return_value=mock_super):
+            et.__del__()
+    finally:
+        # Prevent the exception from being raised during garbage collection
+        mock_super._should_raise = False
+
+
+def test_execute_update_success(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test _execute_update executes query successfully."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    mock_connection = MagicMock()
+    mock_result = MagicMock()
+    mock_result.rowcount = 1
+    mock_connection.execute.return_value = mock_result
+    mock_engine = MagicMock()
+    mock_engine.begin.return_value.__enter__.return_value = mock_connection
+    et.engine = mock_engine
+
+    result = et._execute_update('UPDATE test SET x = :x', {'x': 1})
+
+    assert result == {'rowcount': 1}
+    mock_connection.execute.assert_called_once()
+
+
+def test_update_experiment_run_status_success(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test update_experiment_run with STATUS update type."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking, UpdateType
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    mock_execute = MagicMock()
+
+    def mock_execute_update(*args, **kwargs):
+        mock_execute(*args, **kwargs)
+        return {'rowcount': 1}
+
+    et._execute_update = mock_execute_update  # type: ignore[method-assign]
+    et.info = MagicMock()
+
+    input_data = {
+        'metadata': {'workflow_id': 'test-123'},
+        'experiment_run_id': 1,
+        'update_type': UpdateType.STATUS,
+        'status': 'running',
+    }
+
+    et.update_experiment_run(input_data)
+
+    mock_execute.assert_called_once()
+    call_args = mock_execute.call_args
+    assert 'status' in call_args[0][1]
+    assert call_args[0][1]['status'] == 'running'
+    assert call_args[0][1]['experiment_run_id'] == 1
+    et.info.assert_called_once()
+
+
+def test_update_experiment_run_status_missing_status(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test update_experiment_run with STATUS but missing status parameter."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking, UpdateType
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    et.send_notification = MagicMock()
+
+    input_data = {
+        'metadata': {'workflow_id': 'test-123'},
+        'experiment_run_id': 1,
+        'update_type': UpdateType.STATUS,
+    }
+
+    with pytest.raises(RuntimeError):
+        et.update_experiment_run(input_data)
+
+    et.send_notification.assert_called_once()
+
+
+def test_update_experiment_run_status_with_error_success(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test update_experiment_run with STATUS_WITH_ERROR update type."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking, UpdateType
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    mock_execute = MagicMock()
+
+    def mock_execute_update(*args, **kwargs):
+        mock_execute(*args, **kwargs)
+        return {'rowcount': 1}
+
+    et._execute_update = mock_execute_update  # type: ignore[method-assign]
+    et.info = MagicMock()
+
+    input_data = {
+        'metadata': {'workflow_id': 'test-123'},
+        'experiment_run_id': 1,
+        'update_type': UpdateType.STATUS_WITH_ERROR,
+        'status': 'failed',
+        'error_message': 'Test error',
+    }
+
+    et.update_experiment_run(input_data)
+
+    mock_execute.assert_called_once()
+    call_args = mock_execute.call_args
+    assert 'status' in call_args[0][1]
+    assert call_args[0][1]['status'] == 'failed'
+    assert call_args[0][1]['error_message'] == 'Test error'
+    et.info.assert_called_once()
+
+
+def test_update_experiment_run_status_with_error_truncate_message(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test update_experiment_run truncates error message if too long."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking, UpdateType
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    mock_execute = MagicMock()
+
+    def mock_execute_update(*args, **kwargs):
+        mock_execute(*args, **kwargs)
+        return {'rowcount': 1}
+
+    et._execute_update = mock_execute_update  # type: ignore[method-assign]
+    et.info = MagicMock()
+
+    long_error = 'x' * 2000
+    input_data = {
+        'metadata': {'workflow_id': 'test-123'},
+        'experiment_run_id': 1,
+        'update_type': UpdateType.STATUS_WITH_ERROR,
+        'status': 'failed',
+        'error_message': long_error,
+    }
+
+    et.update_experiment_run(input_data)
+
+    call_args = mock_execute.call_args
+    assert len(call_args[0][1]['error_message']) == 1024
+
+
+def test_update_experiment_run_status_with_error_missing_error_message(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test update_experiment_run with STATUS_WITH_ERROR but missing error_message."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking, UpdateType
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    et.send_notification = MagicMock()
+
+    input_data = {
+        'metadata': {'workflow_id': 'test-123'},
+        'experiment_run_id': 1,
+        'update_type': UpdateType.STATUS_WITH_ERROR,
+        'status': 'failed',
+    }
+
+    with pytest.raises(RuntimeError):
+        et.update_experiment_run(input_data)
+
+    et.send_notification.assert_called_once()
+
+
+def test_update_experiment_run_model_saved_success(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test update_experiment_run with MODEL_SAVED update type."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking, UpdateType
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    mock_execute = MagicMock()
+
+    def mock_execute_update(*args, **kwargs):
+        mock_execute(*args, **kwargs)
+        return {'rowcount': 1}
+
+    et._execute_update = mock_execute_update  # type: ignore[method-assign]
+    et.info = MagicMock()
+
+    input_data = {
+        'metadata': {'workflow_id': 'test-123'},
+        'experiment_run_id': 1,
+        'update_type': UpdateType.MODEL_SAVED,
+        'status': 'completed',
+        'run_name': 'run_001',
+    }
+
+    et.update_experiment_run(input_data)
+
+    mock_execute.assert_called_once()
+    call_args = mock_execute.call_args
+    assert 'run_name' in call_args[0][1]
+    assert call_args[0][1]['run_name'] == 'run_001'
+    assert call_args[0][1]['status'] == 'completed'
+    et.info.assert_called_once()
+
+
+def test_update_experiment_run_model_saved_missing_run_name(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test update_experiment_run with MODEL_SAVED but missing run_name."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking, UpdateType
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    et.send_notification = MagicMock()
+
+    input_data = {
+        'metadata': {'workflow_id': 'test-123'},
+        'experiment_run_id': 1,
+        'update_type': UpdateType.MODEL_SAVED,
+        'status': 'completed',
+    }
+
+    with pytest.raises(RuntimeError):
+        et.update_experiment_run(input_data)
+
+    et.send_notification.assert_called_once()
+
+
+def test_update_experiment_run_invalid_update_type(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test update_experiment_run with invalid update_type."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    et.send_notification = MagicMock()
+
+    input_data = {
+        'metadata': {'workflow_id': 'test-123'},
+        'experiment_run_id': 1,
+        'update_type': 'invalid_type',
+    }
+
+    with pytest.raises(RuntimeError):
+        et.update_experiment_run(input_data)
+
+    et.send_notification.assert_called_once()
+
+
+def test_update_experiment_run_no_rows_updated(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test update_experiment_run raises error when no rows are updated."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking, UpdateType
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    def mock_execute_update(*args, **kwargs):
+        return {'rowcount': 0}
+
+    et._execute_update = mock_execute_update  # type: ignore[method-assign]
+    et.send_notification = MagicMock()
+
+    input_data = {
+        'metadata': {'workflow_id': 'test-123'},
+        'experiment_run_id': 999,
+        'update_type': UpdateType.STATUS,
+        'status': 'running',
+    }
+
+    with pytest.raises(RuntimeError):
+        et.update_experiment_run(input_data)
+
+    et.send_notification.assert_called_once()
+
+
+def test_update_experiment_run_status_with_error_missing_status(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test update_experiment_run with STATUS_WITH_ERROR but missing status - covers line 179."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking, UpdateType
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    et.send_notification = MagicMock()
+
+    input_data = {
+        'metadata': {'workflow_id': 'test-123'},
+        'experiment_run_id': 1,
+        'update_type': UpdateType.STATUS_WITH_ERROR,
+        'error_message': 'Some error',
+    }
+
+    with pytest.raises(RuntimeError):
+        et.update_experiment_run(input_data)
+
+    et.send_notification.assert_called_once()
+
+
+def test_update_experiment_run_model_saved_missing_status(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test update_experiment_run with MODEL_SAVED but missing status - covers line 204."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking, UpdateType
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    et.send_notification = MagicMock()
+
+    input_data = {
+        'metadata': {'workflow_id': 'test-123'},
+        'experiment_run_id': 1,
+        'update_type': UpdateType.MODEL_SAVED,
+        'run_name': 'run_001',
+    }
+
+    with pytest.raises(RuntimeError):
+        et.update_experiment_run(input_data)
+
+    et.send_notification.assert_called_once()
+
+
+def test_experiment_tracking_del_with_engine_no_super_del(
+    db_config, mock_logger, mock_notification_handler, mock_metrics_controller
+):
+    """Test __del__ when engine exists but super has no __del__ - covers line 103."""
+    from model_manager.activities.experiment_tracking import ExperimentTracking
+
+    et = ExperimentTracking(
+        host=db_config['host'],
+        port=db_config['port'],
+        user=db_config['user'],
+        password=db_config['password'],
+        dbname=db_config['dbname'],
+        min_connections=db_config['min_connections'],
+        max_connections=db_config['max_connections'],
+        logger=mock_logger,
+        notification_handler=mock_notification_handler,
+        metrics_controller=mock_metrics_controller,
+    )
+
+    et.engine = MagicMock()
+
+    class MockSuperNoDel:
+        pass
+
+    with patch('builtins.super', return_value=MockSuperNoDel()):
+        et.__del__()

tests/activities/test_training.py

@@ -0,0 +1,630 @@
+"""Unit tests for Training activities."""
+
+from contextlib import contextmanager
+from unittest.mock import MagicMock, patch
+
+import pandas as pd
+import pytest
+
+from model_manager.utils.models.train_model_params import TrainModelParams
+from model_manager.utils.models.train_model_result import TrainModelResult
+
+
+def _minimal_params_dict():
+    return {
+        'variable_columns': ['a'],
+        'target_variable': 't',
+        'bucket_name': 'b',
+        'file_name': 'f.csv',
+        'line_separator': '\n',
+        'decimal_separator': '.',
+        'date_column': 'timestamp',
+        'date_format': 'yyyy-MM-dd HH:mm:ss',
+        'train_size': 80,
+        'shuffle': True,
+        'random_state': 42,
+        'experiment_run_id': 1,
+        'model_name': 'Linear Regression',
+        'val_file_name': None,
+        'data_model_kwargs': {},
+        'model_kwargs': {},
+        'opt_params': {},
+        'model_type': 'linear_regression',
+        'model_id': None,
+        'model_metadata': None,
+    }
+
+
+@pytest.fixture
+def training():
+    from model_manager.activities.training import Training
+
+    return Training(
+        mlflow_repository=MagicMock(),
+        plugin_store=MagicMock(),
+        minio_repository=MagicMock(),
+        logger=MagicMock(),
+        notification_handler=MagicMock(),
+        metrics_controller=MagicMock(),
+    )
+
+
+def test_load_model_metadata_success(training):
+    training.plugin_store.get_model_index = MagicMock(
+        return_value={'schemas': {'components': {'schemas': {}}}}
+    )
+    inp = {**_minimal_params_dict(), 'metadata': {'w': '1'}}
+    out = training.load_model_metadata(inp)
+    assert 'model_metadata' in out
+    assert out['model_metadata']['schemas']
+
+
+def test_load_model_metadata_notifies_on_error(training):
+    training.plugin_store.get_model_index = MagicMock(side_effect=RuntimeError('idx'))
+    training.send_notification = MagicMock()
+    inp = {**_minimal_params_dict(), 'metadata': {}}
+    with pytest.raises(RuntimeError, match='idx'):
+        training.load_model_metadata(inp)
+    training.send_notification.assert_called_once()
+
+
+def test_validate_train_params_success(training):
+    pdict = _minimal_params_dict()
+    pdict['model_metadata'] = {'schemas': {'components': {'schemas': {}}}}
+    inp = {**pdict, 'metadata': {}}
+    out = training.validate_train_params(inp)
+    assert isinstance(out, dict)
+    assert out['target_variable'] == 't'
+
+
+def test_validate_train_params_notifies(training):
+    training.send_notification = MagicMock()
+    inp = {'metadata': {}, 'experiment_run_id': 1}
+    with pytest.raises((KeyError, ValueError, TypeError)):
+        training.validate_train_params(inp)
+    training.send_notification.assert_called_once()
+
+
+def test_train_model_download_fails_notifies(training):
+    """train_model notifies and re-raises when MinIO download fails."""
+    tp = TrainModelParams.from_dict(
+        {
+            **_minimal_params_dict(),
+            'model_metadata': {'schemas': {'components': {'schemas': {}}}},
+        }
+    )
+    training.minio_repository.download_file = MagicMock(side_effect=OSError('minio'))
+    training.send_notification = MagicMock()
+    with pytest.raises(OSError, match='minio'):
+        training.train_model({'metadata': {'pod': 'x'}, 'train_params': tp.to_dict()})
+    training.send_notification.assert_called_once()
+
+
+def test_cleanup_resources(training):
+    training.data_manager_repository.cleanup_run_directory = MagicMock()
+    training.cleanup_resources({'metadata': {}, 'run_dir': '/tmp/x'})
+    training.data_manager_repository.cleanup_run_directory.assert_called_once_with('/tmp/x', {})
+
+
+def test_cleanup_resources_notifies_on_error(training):
+    training.data_manager_repository.cleanup_run_directory = MagicMock(
+        side_effect=RuntimeError('rm')
+    )
+    training.send_notification = MagicMock()
+    with pytest.raises(RuntimeError, match='rm'):
+        training.cleanup_resources({'metadata': {'pod': 'p'}, 'run_dir': '/tmp/x'})
+    training.send_notification.assert_called_once()
+
+
+@patch('model_manager.activities.training.mlflow')
+def test_train_model_success_serializes_result(mock_mlflow, training):
+    """Exercise train_model happy path with mocks (MinIO, plugin wrapper, MLflow)."""
+    tp = TrainModelParams.from_dict(
+        {
+            **_minimal_params_dict(),
+            'model_metadata': {'schemas': {'components': {'schemas': {}}}},
+        }
+    )
+    train_df = pd.DataFrame({'a': [1.0, 2.0], 't': [1.0, 2.0]})
+    val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
+
+    training.minio_repository.download_file = MagicMock(return_value=b'csv')
+    training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
+
+    def _set_metrics(x, _w, **_kw):
+        x.mse_val = 0.1
+        x.mae_val = 0.2
+        x.r2_val = 0.9
+        return x
+
+    training.data_manager_repository.compute_regression_metrics = MagicMock(
+        side_effect=_set_metrics
+    )
+
+    def _fill_report(x, **_kw):
+        x.report_path = '/tmp/report.html'
+        x.train_data_path = '/tmp/train.csv'
+        x.test_data_path = '/tmp/test.csv'
+        x.run_dir = '/tmp/run'
+        return x
+
+    training.data_manager_repository.generate_report = MagicMock(side_effect=_fill_report)
+
+    wrapper = MagicMock()
+    wrapper.transform = MagicMock(side_effect=[(train_df, None), (val_df, None)])
+    pred_train = pd.DataFrame({'p': [1.0, 2.0]})
+    pred_val = pd.DataFrame({'p': [1.0]})
+    wrapper.predict = MagicMock(side_effect=[(pred_train, None), (pred_val, None)])
+    wrapper.store_model = MagicMock()
+    training.plugin_store.get_model = MagicMock(return_value=wrapper)
+
+    @contextmanager
+    def _run_ctx(*_a, **_k):
+        info = MagicMock()
+        info.run_name = 'run-n'
+        info.run_id = 'run-i'
+        yield info
+
+    training.mlflow_repository.start_run = _run_ctx
+
+    out = training.train_model({'metadata': {'pod': 'p'}, 'train_params': tp.to_dict()})
+    assert out['run_name'] is None
+    assert out['run_id'] == 'run-i'
+    assert out['run_dir'] == '/tmp/run'
+    mock_mlflow.log_param.assert_any_call('mse_val', 0.1)
+    mock_mlflow.log_param.assert_any_call('mae_val', 0.2)
+    mock_mlflow.log_param.assert_any_call('r2_val', 0.9)
+    mock_mlflow.log_artifact.assert_called()
+
+
+@patch('model_manager.activities.training.mlflow')
+def test_train_model_without_logger_does_not_set_wrapper_logger(_mock_mlflow, training):
+    """Covers branch where activity logger is None."""
+    training.logger = None
+    tp = TrainModelParams.from_dict(
+        {
+            **_minimal_params_dict(),
+            'model_metadata': {'schemas': {'components': {'schemas': {}}}},
+        }
+    )
+    train_df = pd.DataFrame({'a': [1.0, 2.0], 't': [1.0, 2.0]})
+    val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
+
+    training.minio_repository.download_file = MagicMock(return_value=b'csv')
+    training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
+    training.data_manager_repository.compute_regression_metrics = MagicMock(
+        side_effect=lambda x, _w, **_kw: x
+    )
+
+    def _fill_report(x, **_kw):
+        x.report_path = '/tmp/report.html'
+        x.train_data_path = '/tmp/train.csv'
+        x.test_data_path = '/tmp/test.csv'
+        x.equation_path = '/tmp/eq.json'
+        x.run_dir = '/tmp/run'
+        return x
+
+    training.data_manager_repository.generate_report = MagicMock(side_effect=_fill_report)
+    wrapper = MagicMock()
+    wrapper.transform = MagicMock(side_effect=[(train_df, None), (val_df, None)])
+    wrapper.predict = MagicMock(
+        side_effect=[(pd.DataFrame({'p': [1.0, 2.0]}), None), (pd.DataFrame({'p': [1.0]}), None)]
+    )
+    wrapper.store_model = MagicMock()
+    training.plugin_store.get_model = MagicMock(return_value=wrapper)
+
+    @contextmanager
+    def _run_ctx(*_a, **_k):
+        info = MagicMock()
+        info.run_name = 'n'
+        info.run_id = 'i'
+        yield info
+
+    training.mlflow_repository.start_run = _run_ctx
+
+    training.train_model({'metadata': {}, 'train_params': tp.to_dict()})
+
+
+@patch('model_manager.activities.training.mlflow')
+def test_train_model_train_params_as_dict(mock_mlflow, training):
+    """train_params may arrive as dict and is coerced via TrainModelParams.from_dict."""
+    d = {
+        **_minimal_params_dict(),
+        'model_metadata': {'schemas': {'components': {'schemas': {}}}},
+    }
+    train_df = pd.DataFrame({'a': [1.0, 2.0], 't': [1.0, 2.0]})
+    val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    tp = TrainModelParams.from_dict(d)
+    tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
+
+    training.minio_repository.download_file = MagicMock(return_value=b'csv')
+    training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
+    training.data_manager_repository.compute_regression_metrics = MagicMock(
+        side_effect=lambda x, _w, **_kw: x
+    )
+
+    def _fill_report2(x, **_kw):
+        x.report_path = '/tmp/report.html'
+        x.train_data_path = '/tmp/train.csv'
+        x.test_data_path = '/tmp/test.csv'
+        x.run_dir = '/tmp/run'
+        return x
+
+    training.data_manager_repository.generate_report = MagicMock(side_effect=_fill_report2)
+    wrapper = MagicMock()
+    wrapper.transform = MagicMock(side_effect=[(train_df, None), (val_df, None)])
+    wrapper.predict = MagicMock(
+        side_effect=[(pd.DataFrame({'p': [1.0, 2.0]}), None), (pd.DataFrame({'p': [1.0]}), None)]
+    )
+    wrapper.store_model = MagicMock()
+    training.plugin_store.get_model = MagicMock(return_value=wrapper)
+
+    @contextmanager
+    def _run_ctx(*_a, **_k):
+        info = MagicMock()
+        info.run_name = 'n'
+        info.run_id = 'i'
+        yield info
+
+    training.mlflow_repository.start_run = _run_ctx
+
+    training.train_model({'metadata': {}, 'train_params': d})
+    mock_mlflow.log_artifact.assert_called()
+
+
+@patch('model_manager.activities.training.mlflow')
+def test_train_model_downloads_validation_file_when_set(mock_mlflow, training):
+    """Second MinIO download when val_file_name is set (covers val_bytes branch)."""
+    d = {
+        **_minimal_params_dict(),
+        'val_file_name': 'val.csv',
+        'model_metadata': {'schemas': {'components': {'schemas': {}}}},
+    }
+    tp = TrainModelParams.from_dict(d)
+    train_df = pd.DataFrame({'a': [1.0, 2.0], 't': [1.0, 2.0]})
+    val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
+
+    def _dl(object_name, **_kwargs):
+        if object_name == tp.file_name:
+            return b'train'
+        if object_name == 'val.csv':
+            return b'val'
+        raise AssertionError(object_name)
+
+    training.minio_repository.download_file = MagicMock(side_effect=_dl)
+    training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
+    training.data_manager_repository.compute_regression_metrics = MagicMock(
+        side_effect=lambda x, _w, **_kw: x
+    )
+
+    def _fill(x, **_kw):
+        x.report_path = '/tmp/report.html'
+        x.train_data_path = '/tmp/train.csv'
+        x.test_data_path = '/tmp/test.csv'
+        x.run_dir = '/tmp/run'
+        return x
+
+    training.data_manager_repository.generate_report = MagicMock(side_effect=_fill)
+    wrapper = MagicMock()
+    wrapper.transform = MagicMock(side_effect=[(train_df, None), (val_df, None)])
+    wrapper.predict = MagicMock(
+        side_effect=[(pd.DataFrame({'p': [1.0, 2.0]}), None), (pd.DataFrame({'p': [1.0]}), None)]
+    )
+    wrapper.store_model = MagicMock()
+    training.plugin_store.get_model = MagicMock(return_value=wrapper)
+
+    @contextmanager
+    def _run_ctx(*_a, **_k):
+        info = MagicMock()
+        info.run_name = 'n'
+        info.run_id = 'i'
+        yield info
+
+    training.mlflow_repository.start_run = _run_ctx
+
+    training.train_model({'metadata': {}, 'train_params': tp.to_dict()})
+    assert training.minio_repository.download_file.call_count == 2
+    mock_mlflow.log_artifact.assert_called()
+
+
+def test_prepare_data_observes_lag_on_success(training):
+    tp = TrainModelParams.from_dict(
+        {**_minimal_params_dict(), 'model_metadata': {'schemas': {'components': {'schemas': {}}}}}
+    )
+    train_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
+    training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
+    training.observe_lag_sync = MagicMock()
+    training.emit_metric_sync = MagicMock()
+
+    from model_manager import metrics as mm_metrics
+
+    training._prepare_data(b'csv', None, tp, {})
+
+    training.observe_lag_sync.assert_called_once()
+    call_args = training.observe_lag_sync.call_args
+    assert call_args.args[1] is mm_metrics.SIENTIA_TRAINING_DATA_PREPARATION_LAG
+    training.emit_metric_sync.assert_not_called()
+
+
+def test_prepare_data_increments_error_counter_and_still_observes_lag_on_failure(training):
+    tp = TrainModelParams.from_dict(
+        {**_minimal_params_dict(), 'model_metadata': {'schemas': {'components': {'schemas': {}}}}}
+    )
+    training.data_manager_repository.prepare_training_data = MagicMock(
+        side_effect=RuntimeError('prep-fail')
+    )
+    training.observe_lag_sync = MagicMock()
+    training.emit_metric_sync = MagicMock()
+
+    from model_manager import metrics as mm_metrics
+
+    with pytest.raises(RuntimeError, match='prep-fail'):
+        training._prepare_data(b'csv', None, tp, {})
+
+    training.observe_lag_sync.assert_called_once()
+    training.emit_metric_sync.assert_called_once()
+    call_args = training.emit_metric_sync.call_args
+    assert call_args.kwargs['metric_object'] is mm_metrics.SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL
+
+
+def test_fit_model_observes_lag_on_success(training):
+    tp = TrainModelParams.from_dict(
+        {**_minimal_params_dict(), 'model_metadata': {'schemas': {'components': {'schemas': {}}}}}
+    )
+    train_df = pd.DataFrame({'a': [1.0, 2.0], 't': [1.0, 2.0]})
+    val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
+
+    wrapper = MagicMock()
+    wrapper.transform = MagicMock(side_effect=[(train_df, None), (val_df, None)])
+    wrapper.predict = MagicMock(
+        side_effect=[(pd.DataFrame({'p': [1.0, 2.0]}), None), (pd.DataFrame({'p': [1.0]}), None)]
+    )
+    training.observe_lag_sync = MagicMock()
+    training.emit_metric_sync = MagicMock()
+
+    from model_manager import metrics as mm_metrics
+
+    training._fit_model(wrapper, tmr, tp, {})
+
+    training.observe_lag_sync.assert_called_once()
+    call_args = training.observe_lag_sync.call_args
+    assert call_args.args[1] is mm_metrics.SIENTIA_TRAINING_MODEL_FIT_LAG
+    training.emit_metric_sync.assert_not_called()
+
+
+def test_fit_model_increments_error_counter_on_failure(training):
+    tp = TrainModelParams.from_dict(
+        {**_minimal_params_dict(), 'model_metadata': {'schemas': {'components': {'schemas': {}}}}}
+    )
+    train_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
+
+    wrapper = MagicMock()
+    wrapper.train = MagicMock(side_effect=RuntimeError('fit-fail'))
+    training.observe_lag_sync = MagicMock()
+    training.emit_metric_sync = MagicMock()
+
+    from model_manager import metrics as mm_metrics
+
+    with pytest.raises(RuntimeError, match='fit-fail'):
+        training._fit_model(wrapper, tmr, tp, {})
+
+    training.observe_lag_sync.assert_called_once()
+    training.emit_metric_sync.assert_called_once()
+    call_args = training.emit_metric_sync.call_args
+    assert call_args.kwargs['metric_object'] is mm_metrics.SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL
+
+
+@patch('model_manager.activities.training.mm_metrics')
+@patch('model_manager.activities.training.mlflow')
+def test_train_model_sets_quality_gauges_after_compute_metrics(mock_mlflow, mock_mm_metrics, training):
+    tp = TrainModelParams.from_dict(
+        {**_minimal_params_dict(), 'model_metadata': {'schemas': {'components': {'schemas': {}}}}}
+    )
+    train_df = pd.DataFrame({'a': [1.0, 2.0], 't': [1.0, 2.0]})
+    val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
+
+    training.minio_repository.download_file = MagicMock(return_value=b'csv')
+    training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
+
+    def _set_metrics(x, _w, **_kw):
+        x.mse_val = 0.5
+        x.mae_val = 0.3
+        x.r2_val = -0.1
+        return x
+
+    training.data_manager_repository.compute_regression_metrics = MagicMock(
+        side_effect=_set_metrics
+    )
+
+    def _fill_report(x, **_kw):
+        x.report_path = '/tmp/r.html'
+        x.train_data_path = '/tmp/tr.csv'
+        x.test_data_path = '/tmp/te.csv'
+        x.run_dir = '/tmp/run'
+        return x
+
+    training.data_manager_repository.generate_report = MagicMock(side_effect=_fill_report)
+    wrapper = MagicMock()
+    wrapper.transform = MagicMock(side_effect=[(train_df, None), (val_df, None)])
+    wrapper.predict = MagicMock(
+        side_effect=[(pd.DataFrame({'p': [1.0, 2.0]}), None), (pd.DataFrame({'p': [1.0]}), None)]
+    )
+    wrapper.store_model = MagicMock()
+    training.plugin_store.get_model = MagicMock(return_value=wrapper)
+
+    @contextmanager
+    def _run_ctx(*_a, **_k):
+        info = MagicMock()
+        info.run_id = 'rid'
+        yield info
+
+    training.mlflow_repository.start_run = _run_ctx
+    training.observe_lag_sync = MagicMock()
+    training.emit_metric_sync = MagicMock()
+
+    training.train_model({'metadata': {}, 'train_params': tp.to_dict()})
+
+    mock_mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_MSE.labels.return_value.set.assert_called_once_with(0.5)
+    mock_mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_MAE.labels.return_value.set.assert_called_once_with(0.3)
+    mock_mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_R2.labels.return_value.set.assert_called_once_with(-0.1)
+
+
+@patch('model_manager.activities.training.mm_metrics')
+@patch('model_manager.activities.training.mlflow')
+def test_train_model_skips_quality_gauges_when_none(_mock_mlflow, mock_mm_metrics, training):
+    tp = TrainModelParams.from_dict(
+        {**_minimal_params_dict(), 'model_metadata': {'schemas': {'components': {'schemas': {}}}}}
+    )
+    train_df = pd.DataFrame({'a': [1.0, 2.0], 't': [1.0, 2.0]})
+    val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
+
+    training.minio_repository.download_file = MagicMock(return_value=b'csv')
+    training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
+    training.data_manager_repository.compute_regression_metrics = MagicMock(
+        side_effect=lambda x, _w, **_kw: x
+    )
+
+    def _fill_report(x, **_kw):
+        x.report_path = '/tmp/r.html'
+        x.train_data_path = '/tmp/tr.csv'
+        x.test_data_path = '/tmp/te.csv'
+        x.run_dir = '/tmp/run'
+        return x
+
+    training.data_manager_repository.generate_report = MagicMock(side_effect=_fill_report)
+    wrapper = MagicMock()
+    wrapper.transform = MagicMock(side_effect=[(train_df, None), (val_df, None)])
+    wrapper.predict = MagicMock(
+        side_effect=[(pd.DataFrame({'p': [1.0, 2.0]}), None), (pd.DataFrame({'p': [1.0]}), None)]
+    )
+    wrapper.store_model = MagicMock()
+    training.plugin_store.get_model = MagicMock(return_value=wrapper)
+
+    @contextmanager
+    def _run_ctx(*_a, **_k):
+        info = MagicMock()
+        info.run_id = 'rid'
+        yield info
+
+    training.mlflow_repository.start_run = _run_ctx
+    training.observe_lag_sync = MagicMock()
+    training.emit_metric_sync = MagicMock()
+
+    training.train_model({'metadata': {}, 'train_params': tp.to_dict()})
+
+    mock_mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_MSE.labels.return_value.set.assert_not_called()
+    mock_mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_MAE.labels.return_value.set.assert_not_called()
+    mock_mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_R2.labels.return_value.set.assert_not_called()
+
+
+@patch('model_manager.activities.training.mm_metrics')
+@patch('model_manager.activities.training.mlflow')
+def test_train_model_increments_trained_total_on_success(_mock_mlflow, mock_mm_metrics, training):
+    tp = TrainModelParams.from_dict(
+        {**_minimal_params_dict(), 'model_metadata': {'schemas': {'components': {'schemas': {}}}}}
+    )
+    train_df = pd.DataFrame({'a': [1.0, 2.0], 't': [1.0, 2.0]})
+    val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
+
+    training.minio_repository.download_file = MagicMock(return_value=b'csv')
+    training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
+    training.data_manager_repository.compute_regression_metrics = MagicMock(
+        side_effect=lambda x, _w, **_kw: x
+    )
+
+    def _fill_report(x, **_kw):
+        x.report_path = '/tmp/r.html'
+        x.train_data_path = '/tmp/tr.csv'
+        x.test_data_path = '/tmp/te.csv'
+        x.run_dir = '/tmp/run'
+        return x
+
+    training.data_manager_repository.generate_report = MagicMock(side_effect=_fill_report)
+    wrapper = MagicMock()
+    wrapper.transform = MagicMock(side_effect=[(train_df, None), (val_df, None)])
+    wrapper.predict = MagicMock(
+        side_effect=[(pd.DataFrame({'p': [1.0, 2.0]}), None), (pd.DataFrame({'p': [1.0]}), None)]
+    )
+    wrapper.store_model = MagicMock()
+    training.plugin_store.get_model = MagicMock(return_value=wrapper)
+
+    @contextmanager
+    def _run_ctx(*_a, **_k):
+        info = MagicMock()
+        info.run_id = 'rid'
+        yield info
+
+    training.mlflow_repository.start_run = _run_ctx
+    training.observe_lag_sync = MagicMock()
+    training.emit_metric_sync = MagicMock()
+
+    training.train_model({'metadata': {}, 'train_params': tp.to_dict()})
+
+    training.emit_metric_sync.assert_called_once_with(
+        metric_object=mock_mm_metrics.SIENTIA_TRAINING_MODEL_TRAINED_TOTAL,
+        tags=training._get_training_labels(tp),
+    )
+
+
+def test_train_model_does_not_increment_trained_total_on_failure(training):
+    tp = TrainModelParams.from_dict(
+        {**_minimal_params_dict(), 'model_metadata': {'schemas': {'components': {'schemas': {}}}}}
+    )
+    training.minio_repository.download_file = MagicMock(side_effect=RuntimeError('dl-fail'))
+    training.send_notification = MagicMock()
+    training.emit_metric_sync = MagicMock()
+
+    with pytest.raises(RuntimeError):
+        training.train_model({'metadata': {}, 'train_params': tp.to_dict()})
+
+    training.emit_metric_sync.assert_not_called()
+
+
+def test_train_model_value_error_when_paths_missing_after_report(training):
+    """Raises ValueError when report paths are not populated after generate_report."""
+    tp = TrainModelParams.from_dict(
+        {
+            **_minimal_params_dict(),
+            'model_metadata': {'schemas': {'components': {'schemas': {}}}},
+        }
+    )
+    train_df = pd.DataFrame({'a': [1.0, 2.0], 't': [1.0, 2.0]})
+    val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
+
+    training.minio_repository.download_file = MagicMock(return_value=b'x')
+    training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
+    training.data_manager_repository.compute_regression_metrics = MagicMock(
+        side_effect=lambda x, _w, **_kw: x
+    )
+    training.data_manager_repository.generate_report = MagicMock(return_value=tmr)
+    wrapper = MagicMock()
+    wrapper.transform = MagicMock(side_effect=[(train_df, None), (val_df, None)])
+    wrapper.predict = MagicMock(
+        side_effect=[(pd.DataFrame({'p': [1.0, 2.0]}), None), (pd.DataFrame({'p': [1.0]}), None)]
+    )
+    training.plugin_store.get_model = MagicMock(return_value=wrapper)
+
+    @contextmanager
+    def _run_ctx(*_a, **_k):
+        info = MagicMock()
+        info.run_name = 'n'
+        info.run_id = 'i'
+        yield info
+
+    training.mlflow_repository.start_run = _run_ctx
+    training.send_notification = MagicMock()
+
+    with pytest.raises(ValueError, match='Report path'):
+        training.train_model({'metadata': {}, 'train_params': tp.to_dict()})

tests/conftest.py

@@ -0,0 +1,103 @@
+"""
+Test bootstrap: stub optional `sientia_do` submodules not shipped in minimal installs.
+
+Must run before importing `model_manager.sientia.models` (pulled in via TrainModelParams).
+Stubs Evidently submodules so `model_manager.sientia.reports` imports (via DataManagerRepository).
+"""
+
+from __future__ import annotations
+
+import sys
+from types import ModuleType
+
+
+def _make_dummy(name: str) -> type:
+    return type(name, (), {})
+
+
+def _stub_evidently() -> None:
+    """Minimal Evidently API surface required to import `model_manager.sientia.reports`."""
+    ev = ModuleType('evidently')
+    sys.modules['evidently'] = ev
+
+    mp = ModuleType('evidently.metric_preset')
+    mp.DataDriftPreset = _make_dummy('DataDriftPreset')  # type: ignore[attr-defined]
+    sys.modules['evidently.metric_preset'] = mp
+
+    metrics = ModuleType('evidently.metrics')
+    _metric_names = (
+        'ColumnSummaryMetric',
+        'ConflictTargetMetric',
+        'DatasetCorrelationsMetric',
+        'DatasetSummaryMetric',
+        'RegressionAbsPercentageErrorPlot',
+        'RegressionDummyMetric',
+        'RegressionErrorDistribution',
+        'RegressionErrorPlot',
+        'RegressionPerformanceMetrics',
+        'RegressionPredictedVsActualPlot',
+        'RegressionPredictedVsActualScatter',
+    )
+    for n in _metric_names:
+        setattr(metrics, n, _make_dummy(n))
+    sys.modules['evidently.metrics'] = metrics
+
+    base = ModuleType('evidently.metrics.base_metric')
+
+    def generate_column_metrics(*_a, **_k):
+        return []
+
+    base.generate_column_metrics = generate_column_metrics  # type: ignore[attr-defined]
+    sys.modules['evidently.metrics.base_metric'] = base
+
+    opt = ModuleType('evidently.options')
+    opt.ColorOptions = _make_dummy('ColorOptions')  # type: ignore[attr-defined]
+    sys.modules['evidently.options'] = opt
+
+    pipeline = ModuleType('evidently.pipeline')
+    sys.modules['evidently.pipeline'] = pipeline
+
+    colmap = ModuleType('evidently.pipeline.column_mapping')
+    colmap.ColumnMapping = _make_dummy('ColumnMapping')  # type: ignore[attr-defined]
+    sys.modules['evidently.pipeline.column_mapping'] = colmap
+
+    rep = ModuleType('evidently.report')
+    rep.Report = _make_dummy('Report')  # type: ignore[attr-defined]
+    sys.modules['evidently.report'] = rep
+
+
+def pytest_configure(config) -> None:  # noqa: ARG001
+    """Register stub modules so imports used by production code resolve in CI/dev venvs."""
+    _stub_evidently()
+
+    if 'sientia_do.operations.df_preprocessor' not in sys.modules:
+        df_pre = ModuleType('sientia_do.operations.df_preprocessor')
+
+        def create_features(input_data, *_a, **_k):
+            return input_data
+
+        def limit_dataset(input_data, low_lim, upp_lim, *_a, **_k):
+            return input_data, low_lim, upp_lim
+
+        def treat_nan(input_data, *_a, **_k):
+            return input_data
+
+        df_pre.create_features = create_features  # type: ignore[attr-defined]
+        df_pre.limit_dataset = limit_dataset  # type: ignore[attr-defined]
+        df_pre.treat_nan = treat_nan  # type: ignore[attr-defined]
+        sys.modules['sientia_do.operations.df_preprocessor'] = df_pre
+
+    sys.modules.setdefault('sientia_do.operations', ModuleType('sientia_do.operations'))
+
+    if 'sientia_do.timeseries.analyzer' not in sys.modules:
+        ts_an = ModuleType('sientia_do.timeseries.analyzer')
+
+        class TimeSeriesDiscontinuityAnalyzer:  # noqa: D401
+            """Stub for tests."""
+
+            pass
+
+        ts_an.TimeSeriesDiscontinuityAnalyzer = TimeSeriesDiscontinuityAnalyzer  # type: ignore[attr-defined]
+        sys.modules['sientia_do.timeseries.analyzer'] = ts_an
+
+    sys.modules.setdefault('sientia_do.timeseries', ModuleType('sientia_do.timeseries'))

tests/schedules/test_cleanup_schedule.py

@@ -0,0 +1,469 @@
+"""Tests for cleanup schedule management."""
+
+import os
+from datetime import timedelta
+from importlib import reload
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+
+@pytest.fixture
+def mock_temporal_client():
+    """Fixture for a mock Temporal client."""
+    client = AsyncMock()
+    client.list_schedules = AsyncMock()
+    client.create_schedule = AsyncMock()
+    handle = AsyncMock()
+    handle.delete = AsyncMock()
+    schedule = MagicMock()
+    schedule.action.task_queue = 'cleanup_files-model-manager-worker-queue'
+    schedule.action.execution_timeout = timedelta(hours=1)
+    schedule.spec.cron_expressions = ['0 0 * * *']
+    schedule.spec.time_zone_name = 'UTC'
+    handle.describe = AsyncMock(return_value=MagicMock(schedule=schedule))
+    client.get_schedule_handle = MagicMock(return_value=handle)
+    return client
+
+
+@pytest.fixture
+def mock_logger():
+    """Fixture for a mock Sientia logger."""
+    logger = MagicMock()
+    logger.custom_info = MagicMock()
+    logger.custom_error = MagicMock()
+    return logger
+
+
+@pytest.fixture
+def metadata():
+    """Fixture for metadata dict."""
+    return {'pod_id': 'test-pod', 'project_name': 'test-project'}
+
+
+# --- schedule_exists Tests ---
+
+
+@pytest.mark.asyncio
+async def test_schedule_exists_returns_true_when_schedule_found(
+    mock_temporal_client, mock_logger, metadata
+):
+    """Test that schedule_exists returns True when schedule is found."""
+    from model_manager.schedules.cleanup_schedule import schedule_exists
+
+    # Mock schedule list with matching schedule
+    mock_schedule = MagicMock()
+    mock_schedule.id = 'test-schedule-id'
+
+    async def mock_list_schedules():
+        yield mock_schedule
+
+    mock_temporal_client.list_schedules.return_value = mock_list_schedules()
+
+    result = await schedule_exists(mock_temporal_client, 'test-schedule-id', mock_logger, metadata)
+
+    assert result is True
+    mock_temporal_client.list_schedules.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_schedule_exists_returns_false_when_schedule_not_found(
+    mock_temporal_client, mock_logger, metadata
+):
+    """Test that schedule_exists returns False when schedule is not found."""
+    from model_manager.schedules.cleanup_schedule import schedule_exists
+
+    # Mock empty schedule list
+    async def mock_list_schedules():
+        return
+        yield  # Make it an async generator
+
+    mock_temporal_client.list_schedules.return_value = mock_list_schedules()
+
+    result = await schedule_exists(
+        mock_temporal_client, 'nonexistent-schedule', mock_logger, metadata
+    )
+
+    assert result is False
+    mock_temporal_client.list_schedules.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_schedule_exists_returns_false_when_different_schedule_found(
+    mock_temporal_client, mock_logger, metadata
+):
+    """Test that schedule_exists returns False when only different schedules exist."""
+    from model_manager.schedules.cleanup_schedule import schedule_exists
+
+    # Mock schedule list with non-matching schedule
+    mock_schedule = MagicMock()
+    mock_schedule.id = 'different-schedule-id'
+
+    async def mock_list_schedules():
+        yield mock_schedule
+
+    mock_temporal_client.list_schedules.return_value = mock_list_schedules()
+
+    result = await schedule_exists(mock_temporal_client, 'test-schedule-id', mock_logger, metadata)
+
+    assert result is False
+    mock_temporal_client.list_schedules.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_schedule_exists_handles_exception(mock_temporal_client, mock_logger, metadata):
+    """Test that schedule_exists handles exceptions gracefully."""
+    from model_manager.schedules.cleanup_schedule import schedule_exists
+
+    # Mock list_schedules to raise an exception
+    mock_temporal_client.list_schedules.side_effect = Exception('Connection error')
+
+    result = await schedule_exists(mock_temporal_client, 'test-schedule-id', mock_logger, metadata)
+
+    assert result is False
+    mock_logger.custom_error.assert_called_once()
+    assert 'Error checking if schedule exists' in mock_logger.custom_error.call_args[0][0]
+
+
+@pytest.mark.asyncio
+async def test_needs_schedule_reconcile_handles_describe_exception(mock_logger, metadata):
+    """Test _needs_schedule_reconcile returns True and logs when describe fails."""
+    from model_manager.schedules.cleanup_schedule import _needs_schedule_reconcile
+
+    handle = AsyncMock()
+    handle.describe = AsyncMock(side_effect=RuntimeError('describe failed'))
+
+    needs_reconcile = await _needs_schedule_reconcile(
+        schedule_handle=handle,
+        cleanup_task_queue='cleanup_files-model-manager-worker-queue',
+        logger=mock_logger,
+        metadata=metadata,
+    )
+
+    assert needs_reconcile is True
+    mock_logger.custom_error.assert_called_once()
+    assert (
+        'Error describing cleanup schedule for reconcile'
+        in mock_logger.custom_error.call_args[0][0]
+    )
+
+
+# --- create_cleanup_schedule Tests ---
+
+
+@pytest.mark.asyncio
+@patch.dict(
+    'model_manager.schedules.cleanup_schedule.os.environ',
+    {
+        'RUNTIME': 'model-manager-worker',
+    },
+)
+async def test_create_cleanup_schedule_reconciles_when_exists(
+    mock_temporal_client, mock_logger, metadata
+):
+    """Test that create_cleanup_schedule recreates schedule when it already exists."""
+    import model_manager.schedules.cleanup_schedule
+
+    reload(model_manager.schedules.cleanup_schedule)
+    from model_manager.schedules.cleanup_schedule import create_cleanup_schedule
+
+    # Mock schedule already exists
+    mock_schedule = MagicMock()
+    mock_schedule.id = 'cleanup-files-model-manager-worker-daily'
+
+    async def mock_list_schedules():
+        yield mock_schedule
+
+    mock_temporal_client.list_schedules.return_value = mock_list_schedules()
+
+    # Force reconcile by diverging task queue
+    mock_temporal_client.get_schedule_handle.return_value.describe.return_value.schedule.action.task_queue = 'different-queue'
+
+    await create_cleanup_schedule(mock_temporal_client, mock_logger, metadata)
+
+    # Verify schedule was reconciled via delete + create
+    mock_temporal_client.get_schedule_handle.assert_called_once_with(
+        'cleanup-files-model-manager-worker-daily'
+    )
+    mock_temporal_client.get_schedule_handle.return_value.delete.assert_called_once()
+    mock_temporal_client.create_schedule.assert_called_once()
+
+    mock_logger.custom_info.assert_called_once()
+    assert 'reconciled successfully' in mock_logger.custom_info.call_args[0][0]
+
+
+@pytest.mark.asyncio
+@patch.dict(
+    'model_manager.schedules.cleanup_schedule.os.environ',
+    {
+        'RUNTIME': 'model-manager-worker',
+    },
+)
+async def test_create_cleanup_schedule_noop_when_schedule_is_up_to_date(
+    mock_temporal_client, mock_logger, metadata
+):
+    """Test no-op reconcile when existing schedule already matches current config."""
+    import model_manager.schedules.cleanup_schedule
+
+    reload(model_manager.schedules.cleanup_schedule)
+    from model_manager.schedules.cleanup_schedule import create_cleanup_schedule
+
+    mock_schedule = MagicMock()
+    mock_schedule.id = 'cleanup-files-model-manager-worker-daily'
+
+    async def mock_list_schedules():
+        yield mock_schedule
+
+    mock_temporal_client.list_schedules.return_value = mock_list_schedules()
+
+    await create_cleanup_schedule(mock_temporal_client, mock_logger, metadata)
+
+    mock_temporal_client.get_schedule_handle.assert_called_once_with(
+        'cleanup-files-model-manager-worker-daily'
+    )
+    mock_temporal_client.get_schedule_handle.return_value.delete.assert_not_called()
+    mock_temporal_client.create_schedule.assert_not_called()
+    mock_logger.custom_info.assert_called_once()
+    assert 'no-op reconcile' in mock_logger.custom_info.call_args[0][0]
+
+
+@pytest.mark.asyncio
+@patch.dict(
+    'model_manager.schedules.cleanup_schedule.os.environ',
+    {
+        'RUNTIME': 'model-manager-worker',
+        'CLEANUP_CRON': '0 2 * * *',
+        'CLEANUP_TIMEZONE': 'America/Sao_Paulo',
+        'CLEANUP_EXECUTION_TIMEOUT_HOURS': '2',
+    },
+)
+async def test_create_cleanup_schedule_creates_with_custom_config(
+    mock_temporal_client, mock_logger, metadata
+):
+    """Test that create_cleanup_schedule creates schedule with custom configuration."""
+    import model_manager.schedules.cleanup_schedule
+
+    reload(model_manager.schedules.cleanup_schedule)
+    from model_manager.schedules.cleanup_schedule import create_cleanup_schedule
+
+    # Mock schedule does not exist (empty list)
+    async def mock_list_schedules():
+        return
+        yield  # Make it an async generator
+
+    mock_temporal_client.list_schedules.return_value = mock_list_schedules()
+
+    await create_cleanup_schedule(mock_temporal_client, mock_logger, metadata)
+
+    # Verify schedule creation was called
+    mock_temporal_client.create_schedule.assert_called_once()
+
+    # Verify schedule parameters
+    call_args = mock_temporal_client.create_schedule.call_args
+    schedule_id = call_args[0][0]
+    schedule_obj = call_args[0][1]
+
+    assert schedule_id == 'cleanup-files-model-manager-worker-daily'
+    assert schedule_obj.action.workflow == 'cleanup_files'
+    assert schedule_obj.action.task_queue == 'cleanup_files-model-manager-worker-queue'
+    assert schedule_obj.action.execution_timeout == timedelta(hours=2)
+    assert schedule_obj.spec.cron_expressions == ['0 2 * * *']
+    assert schedule_obj.spec.time_zone_name == 'America/Sao_Paulo'
+
+    # Verify success log was called
+    assert mock_logger.custom_info.call_count == 1
+    assert 'created successfully' in mock_logger.custom_info.call_args[0][0]
+
+
+@pytest.mark.asyncio
+@patch.dict(
+    'model_manager.schedules.cleanup_schedule.os.environ',
+    {
+        'RUNTIME': 'model-manager-worker',
+    },
+)
+async def test_create_cleanup_schedule_uses_defaults(mock_temporal_client, mock_logger, metadata):
+    """Test that create_cleanup_schedule uses default values when env vars not set."""
+    import model_manager.schedules.cleanup_schedule
+
+    # Remove optional env vars to test defaults
+    for key in [
+        'CLEANUP_CRON',
+        'CLEANUP_TIMEZONE',
+        'CLEANUP_EXECUTION_TIMEOUT_HOURS',
+    ]:
+        os.environ.pop(key, None)
+
+    reload(model_manager.schedules.cleanup_schedule)
+    from model_manager.schedules.cleanup_schedule import create_cleanup_schedule
+
+    # Mock schedule does not exist (empty list)
+    async def mock_list_schedules():
+        return
+        yield  # Make it an async generator
+
+    mock_temporal_client.list_schedules.return_value = mock_list_schedules()
+
+    await create_cleanup_schedule(mock_temporal_client, mock_logger, metadata)
+
+    # Verify schedule creation was called
+    mock_temporal_client.create_schedule.assert_called_once()
+
+    # Verify default parameters
+    call_args = mock_temporal_client.create_schedule.call_args
+    schedule_obj = call_args[0][1]
+
+    assert schedule_obj.spec.cron_expressions == ['0 0 * * *']  # Default midnight
+    assert schedule_obj.spec.time_zone_name == 'UTC'  # Default UTC
+    assert schedule_obj.action.task_queue == 'cleanup_files-model-manager-worker-queue'
+    assert schedule_obj.action.execution_timeout == timedelta(hours=1)  # Default 1 hour
+
+
+@pytest.mark.asyncio
+@patch.dict(
+    'model_manager.schedules.cleanup_schedule.os.environ',
+    {
+        'RUNTIME': 'model-manager-worker',
+    },
+)
+async def test_create_cleanup_schedule_workflow_id_format(
+    mock_temporal_client, mock_logger, metadata
+):
+    """Test that workflow ID is correctly formatted with schedule ID."""
+    import model_manager.schedules.cleanup_schedule
+
+    reload(model_manager.schedules.cleanup_schedule)
+    from model_manager.schedules.cleanup_schedule import create_cleanup_schedule
+
+    # Mock schedule does not exist (empty list)
+    async def mock_list_schedules():
+        return
+        yield  # Make it an async generator
+
+    mock_temporal_client.list_schedules.return_value = mock_list_schedules()
+
+    await create_cleanup_schedule(mock_temporal_client, mock_logger, metadata)
+
+    # Verify workflow ID format
+    call_args = mock_temporal_client.create_schedule.call_args
+    schedule_obj = call_args[0][1]
+
+    expected_workflow_id = 'cleanup-files-scheduled-cleanup-files-model-manager-worker-daily'
+    assert schedule_obj.action.id == expected_workflow_id
+
+
+@pytest.mark.asyncio
+@patch.dict(
+    'model_manager.schedules.cleanup_schedule.os.environ',
+    {
+        'RUNTIME': 'model-manager-worker',
+    },
+)
+async def test_create_cleanup_schedule_empty_workflow_args(
+    mock_temporal_client, mock_logger, metadata
+):
+    """Test that workflow is created with empty args (uses env defaults)."""
+    import model_manager.schedules.cleanup_schedule
+
+    reload(model_manager.schedules.cleanup_schedule)
+    from model_manager.schedules.cleanup_schedule import create_cleanup_schedule
+
+    # Mock schedule does not exist (empty list)
+    async def mock_list_schedules():
+        return
+        yield  # Make it an async generator
+
+    mock_temporal_client.list_schedules.return_value = mock_list_schedules()
+
+    await create_cleanup_schedule(mock_temporal_client, mock_logger, metadata)
+
+    # Verify workflow args are empty (it's a list with one empty dict)
+    call_args = mock_temporal_client.create_schedule.call_args
+    schedule_obj = call_args[0][1]
+
+    # The args are passed as positional args, so it's a list with one element
+    assert schedule_obj.action.args == [{}]
+
+
+# --- Environment Variable Configuration Tests ---
+
+
+@patch.dict(
+    'model_manager.schedules.cleanup_schedule.os.environ',
+    {
+        'RUNTIME': 'model-manager-worker',
+        'CLEANUP_CRON': '30 3 * * 1',
+        'CLEANUP_TIMEZONE': 'Europe/London',
+        'CLEANUP_EXECUTION_TIMEOUT_HOURS': '3',
+    },
+)
+def test_environment_variables_loaded_correctly():
+    """Test that environment variables are loaded correctly."""
+    import model_manager.schedules.cleanup_schedule
+
+    reload(model_manager.schedules.cleanup_schedule)
+    from model_manager.schedules.cleanup_schedule import (
+        CLEANUP_CRON,
+        CLEANUP_EXECUTION_TIMEOUT_HOURS,
+        CLEANUP_TIMEZONE,
+        build_cleanup_schedule_id,
+    )
+
+    assert (
+        build_cleanup_schedule_id('model-manager-worker')
+        == 'cleanup-files-model-manager-worker-daily'
+    )
+    assert CLEANUP_CRON == '30 3 * * 1'
+    assert CLEANUP_TIMEZONE == 'Europe/London'
+    assert CLEANUP_EXECUTION_TIMEOUT_HOURS == 3
+
+
+def test_environment_variables_use_defaults_when_not_set():
+    """Test that default values are used when environment variables are not set."""
+    import model_manager.schedules.cleanup_schedule
+
+    # Remove all env vars
+    for key in [
+        'RUNTIME',
+        'CLEANUP_CRON',
+        'CLEANUP_TIMEZONE',
+        'CLEANUP_EXECUTION_TIMEOUT_HOURS',
+    ]:
+        os.environ.pop(key, None)
+
+    reload(model_manager.schedules.cleanup_schedule)
+    from model_manager.schedules.cleanup_schedule import (
+        CLEANUP_CRON,
+        CLEANUP_EXECUTION_TIMEOUT_HOURS,
+        CLEANUP_TIMEZONE,
+        build_cleanup_schedule_id,
+    )
+
+    assert build_cleanup_schedule_id(None) == 'cleanup-files-single-daily'
+    assert CLEANUP_CRON == '0 0 * * *'
+    assert CLEANUP_TIMEZONE == 'UTC'
+    assert CLEANUP_EXECUTION_TIMEOUT_HOURS == 1
+
+
+@pytest.mark.asyncio
+async def test_create_cleanup_schedule_uses_single_runtime_when_runtime_missing(
+    mock_temporal_client, mock_logger, metadata
+):
+    """Test create_cleanup_schedule uses single runtime fallback."""
+    import model_manager.schedules.cleanup_schedule
+
+    os.environ.pop('RUNTIME', None)
+    reload(model_manager.schedules.cleanup_schedule)
+    from model_manager.schedules.cleanup_schedule import create_cleanup_schedule
+
+    async def mock_list_schedules():
+        return
+        yield
+
+    mock_temporal_client.list_schedules.return_value = mock_list_schedules()
+
+    await create_cleanup_schedule(mock_temporal_client, mock_logger, metadata)
+
+    call_args = mock_temporal_client.create_schedule.call_args
+    schedule_obj = call_args[0][1]
+    assert schedule_obj.action.task_queue == 'cleanup_files-single-queue'

tests/sientia/test_exceptions.py

@@ -0,0 +1,9 @@
+"""Unit tests for custom exception aliases."""
+
+from mlflow.exceptions import MlflowException
+
+from model_manager.sientia.exceptions import SientiaMlException
+
+
+def test_sientia_ml_exception_is_mlflow_exception_alias():
+    assert SientiaMlException is MlflowException

tests/sientia/test_metrics.py

@@ -0,0 +1,481 @@
+"""Unit tests for sientia metrics module."""
+
+import numpy as np
+import pandas as pd
+
+from model_manager.sientia.metrics import (
+    mae,
+    mse,
+    r2,
+    rce_drift,
+    rce_test,
+    rce_train,
+    silverman_radius,
+)
+
+
+def test_mse_perfect_predictions():
+    """Test MSE with perfect predictions returns 0.0."""
+    real_data = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
+    predictions = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
+
+    result = mse(real_data, predictions)
+
+    assert result == 0.0
+
+
+def test_mse_with_errors():
+    """Test MSE calculation with prediction errors."""
+    real_data = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
+    predictions = pd.Series([1.5, 2.5, 3.5, 4.5, 5.5])
+
+    result = mse(real_data, predictions)
+
+    # MSE = mean((0.5^2, 0.5^2, 0.5^2, 0.5^2, 0.5^2)) = 0.25
+    assert result == 0.25
+
+
+def test_mse_with_integer_input():
+    """Test MSE handles integer input and converts to float64."""
+    real_data = pd.Series([1, 2, 3, 4, 5])
+    predictions = pd.Series([2, 3, 4, 5, 6])
+
+    result = mse(real_data, predictions)
+
+    # MSE = mean((1^2, 1^2, 1^2, 1^2, 1^2)) = 1.0
+    assert result == 1.0
+
+
+def test_mse_with_large_errors():
+    """Test MSE with large prediction errors."""
+    real_data = pd.Series([10.0, 20.0, 30.0])
+    predictions = pd.Series([5.0, 15.0, 25.0])
+
+    result = mse(real_data, predictions)
+
+    # MSE = mean((25, 25, 25)) = 25.0
+    assert result == 25.0
+
+
+def test_mse_rounds_to_two_decimals():
+    """Test MSE rounds result to 2 decimal places."""
+    real_data = pd.Series([1.111, 2.222, 3.333])
+    predictions = pd.Series([1.222, 2.333, 3.444])
+
+    result = mse(real_data, predictions)
+
+    # Result should be rounded to 2 decimals
+    assert isinstance(result, float)
+    assert len(str(result).split('.')[-1]) <= 2
+
+
+def test_mae_perfect_predictions():
+    """Test MAE with perfect predictions returns 0.0."""
+    real_data = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
+    predictions = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
+
+    result = mae(real_data, predictions)
+
+    assert result == 0.0
+
+
+def test_mae_with_errors():
+    """Test MAE calculation with prediction errors."""
+    real_data = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
+    predictions = pd.Series([1.5, 2.5, 3.5, 4.5, 5.5])
+
+    result = mae(real_data, predictions)
+
+    # MAE = mean(|0.5|, |0.5|, |0.5|, |0.5|, |0.5|) = 0.5
+    assert result == 0.5
+
+
+def test_mae_with_integer_input():
+    """Test MAE handles integer input and converts to float64."""
+    real_data = pd.Series([1, 2, 3, 4, 5])
+    predictions = pd.Series([2, 3, 4, 5, 6])
+
+    result = mae(real_data, predictions)
+
+    # MAE = mean(|1|, |1|, |1|, |1|, |1|) = 1.0
+    assert result == 1.0
+
+
+def test_mae_with_negative_errors():
+    """Test MAE with negative prediction errors (absolute value)."""
+    real_data = pd.Series([10.0, 20.0, 30.0])
+    predictions = pd.Series([15.0, 25.0, 35.0])
+
+    result = mae(real_data, predictions)
+
+    # MAE = mean(|5|, |5|, |5|) = 5.0
+    assert result == 5.0
+
+
+def test_mae_rounds_to_two_decimals():
+    """Test MAE rounds result to 2 decimal places."""
+    real_data = pd.Series([1.111, 2.222, 3.333])
+    predictions = pd.Series([1.222, 2.333, 3.444])
+
+    result = mae(real_data, predictions)
+
+    # Result should be rounded to 2 decimals
+    assert isinstance(result, float)
+    assert len(str(result).split('.')[-1]) <= 2
+
+
+def test_r2_perfect_predictions():
+    """Test R2 with perfect predictions returns 1.0."""
+    real_data = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
+    predictions = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
+
+    result = r2(real_data, predictions)
+
+    assert result == 1.0
+
+
+def test_r2_with_good_predictions():
+    """Test R2 calculation with good predictions."""
+    real_data = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
+    predictions = pd.Series([1.1, 2.1, 2.9, 4.1, 4.9])
+
+    result = r2(real_data, predictions)
+
+    # R2 should be close to 1.0 for good predictions
+    assert result > 0.9
+    assert result <= 1.0
+
+
+def test_r2_with_integer_input():
+    """Test R2 handles integer input and converts to float64."""
+    real_data = pd.Series([1, 2, 3, 4, 5])
+    predictions = pd.Series([1, 2, 3, 4, 5])
+
+    result = r2(real_data, predictions)
+
+    assert result == 1.0
+
+
+def test_r2_with_poor_predictions():
+    """Test R2 with poor predictions returns low score."""
+    real_data = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
+    predictions = pd.Series([5.0, 4.0, 3.0, 2.0, 1.0])
+
+    result = r2(real_data, predictions)
+
+    # R2 should be negative for predictions worse than mean
+    assert result < 0
+
+
+def test_r2_rounds_to_two_decimals():
+    """Test R2 rounds result to 2 decimal places."""
+    real_data = pd.Series([1.111, 2.222, 3.333, 4.444, 5.555])
+    predictions = pd.Series([1.222, 2.333, 3.444, 4.555, 5.666])
+
+    result = r2(real_data, predictions)
+
+    # Result should be rounded to 2 decimals
+    assert isinstance(result, float)
+    assert len(str(result).split('.')[-1]) <= 2
+
+
+def test_mse_with_mixed_positive_negative():
+    """Test MSE with mixed positive and negative values."""
+    real_data = pd.Series([-5.0, -2.0, 0.0, 3.0, 7.0])
+    predictions = pd.Series([-4.0, -1.0, 1.0, 4.0, 8.0])
+
+    result = mse(real_data, predictions)
+
+    # MSE = mean((1^2, 1^2, 1^2, 1^2, 1^2)) = 1.0
+    assert result == 1.0
+
+
+def test_mae_with_mixed_positive_negative():
+    """Test MAE with mixed positive and negative values."""
+    real_data = pd.Series([-5.0, -2.0, 0.0, 3.0, 7.0])
+    predictions = pd.Series([-4.0, -1.0, 1.0, 4.0, 8.0])
+
+    result = mae(real_data, predictions)
+
+    # MAE = mean(|1|, |1|, |1|, |1|, |1|) = 1.0
+    assert result == 1.0
+
+
+def test_r2_with_mixed_positive_negative():
+    """Test R2 with mixed positive and negative values."""
+    real_data = pd.Series([-5.0, -2.0, 0.0, 3.0, 7.0])
+    predictions = pd.Series([-5.0, -2.0, 0.0, 3.0, 7.0])
+
+    result = r2(real_data, predictions)
+
+    assert result == 1.0
+
+
+# ============================================================================
+# Tests for silverman_radius
+# ============================================================================
+
+
+def test_silverman_radius_basic():
+    """Test silverman_radius returns a positive float."""
+    data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
+
+    result = silverman_radius(data)
+
+    assert isinstance(result, float)
+    assert result > 0
+
+
+def test_silverman_radius_uniform_data():
+    """Test silverman_radius with uniformly distributed data."""
+    data = np.linspace(0, 100, 50)
+
+    result = silverman_radius(data)
+
+    assert result > 0
+    assert np.isfinite(result)
+
+
+def test_silverman_radius_normal_distribution():
+    """Test silverman_radius with normally distributed data."""
+    np.random.seed(42)
+    data = np.random.normal(loc=50, scale=10, size=100)
+
+    result = silverman_radius(data)
+
+    assert result > 0
+    assert np.isfinite(result)
+
+
+def test_silverman_radius_small_dataset():
+    """Test silverman_radius with small dataset."""
+    data = np.array([1.0, 2.0, 3.0])
+
+    result = silverman_radius(data)
+
+    assert result > 0
+
+
+# ============================================================================
+# Tests for rce_train
+# ============================================================================
+
+
+def test_rce_train_returns_dataframe():
+    """Test rce_train returns a DataFrame."""
+    training_set = pd.DataFrame({'a': [1.0, 2.0, 3.0, 4.0, 5.0], 'b': [2.0, 3.0, 4.0, 5.0, 6.0]})
+
+    result = rce_train(training_set, 0.1)
+
+    assert isinstance(result, pd.DataFrame)
+
+
+def test_rce_train_includes_first_vector():
+    """Test rce_train always includes the first vector as a prototype."""
+    training_set = pd.DataFrame({'a': [1.0, 2.0, 3.0], 'b': [1.0, 2.0, 3.0]})
+
+    result = rce_train(training_set, 0.1)
+
+    assert len(result) >= 1
+    assert result.iloc[0].tolist() == [1.0, 1.0]
+
+
+def test_rce_train_with_identical_vectors():
+    """Test rce_train with identical vectors returns single prototype."""
+    training_set = pd.DataFrame({'a': [1.0, 1.0, 1.0], 'b': [2.0, 2.0, 2.0]})
+
+    result = rce_train(training_set, 0.1)
+
+    # All vectors are identical, so only one prototype should be created
+    assert len(result) == 1
+
+
+def test_rce_train_with_distant_vectors():
+    """Test rce_train with very distant vectors creates multiple prototypes."""
+    training_set = pd.DataFrame({'a': [0.0, 100.0, 200.0], 'b': [0.0, 100.0, 200.0]})
+
+    result = rce_train(training_set, 0.1)
+
+    # Distant vectors should create multiple prototypes
+    assert len(result) >= 1
+
+
+# ============================================================================
+# Tests for rce_test
+# ============================================================================
+
+
+def test_rce_test_returns_series():
+    """Test rce_test returns a pandas Series."""
+    test_set = pd.DataFrame({'a': [1.5, 2.5], 'b': [1.5, 2.5]})
+    prototypes = pd.DataFrame({'a': [1.0, 3.0], 'b': [1.0, 3.0]})
+
+    result = rce_test(test_set, prototypes)
+
+    assert isinstance(result, pd.Series)
+    assert len(result) == len(test_set)
+
+
+def test_rce_test_with_exact_match():
+    """Test rce_test with test vector matching a prototype."""
+    test_set = pd.DataFrame({'a': [1.0], 'b': [2.0]})
+    prototypes = pd.DataFrame({'a': [1.0], 'b': [2.0]})
+
+    result = rce_test(test_set, prototypes)
+
+    # Distance should be 0 for exact match
+    assert result.iloc[0] == 0.0
+
+
+def test_rce_test_multiple_prototypes():
+    """Test rce_test finds closest prototype."""
+    test_set = pd.DataFrame({'a': [1.1], 'b': [1.1]})
+    prototypes = pd.DataFrame({'a': [1.0, 10.0], 'b': [1.0, 10.0]})
+
+    result = rce_test(test_set, prototypes)
+
+    # Should find the closest prototype (1.0, 1.0)
+    assert len(result) == 1
+    assert np.isfinite(result.iloc[0])
+
+
+def test_rce_test_signed_distances():
+    """Test rce_test returns signed distances."""
+    test_set = pd.DataFrame({'a': [0.0, 5.0], 'b': [0.0, 5.0]})
+    prototypes = pd.DataFrame({'a': [2.0], 'b': [2.0]})
+
+    result = rce_test(test_set, prototypes)
+
+    assert len(result) == 2
+    # First test vector (0,0) is less than prototype (2,2) - should be negative
+    # Second test vector (5,5) is greater than prototype (2,2) - should be positive
+    assert result.iloc[0] < 0
+    assert result.iloc[1] > 0
+
+
+# ============================================================================
+# Tests for rce_drift
+# ============================================================================
+
+
+def test_rce_drift_returns_series():
+    """Test rce_drift returns a pandas Series."""
+    reference_data = pd.DataFrame(
+        {
+            'feature1': [1.0, 2.0, 3.0, 4.0, 5.0],
+            'feature2': [2.0, 3.0, 4.0, 5.0, 6.0],
+            'target': [10.0, 20.0, 30.0, 40.0, 50.0],
+            'prediction': [11.0, 21.0, 31.0, 41.0, 51.0],
+        }
+    )
+    real_data = pd.DataFrame(
+        {
+            'feature1': [1.5, 2.5],
+            'feature2': [2.5, 3.5],
+            'target': [15.0, 25.0],
+            'prediction': [16.0, 26.0],
+        }
+    )
+
+    result = rce_drift(reference_data, real_data, 'target')
+
+    assert isinstance(result, pd.Series)
+    assert len(result) == len(real_data)
+
+
+def test_rce_drift_with_target_column():
+    """Test rce_drift using target column (drops prediction)."""
+    reference_data = pd.DataFrame(
+        {
+            'feature1': [1.0, 2.0, 3.0],
+            'target': [10.0, 20.0, 30.0],
+            'prediction': [11.0, 21.0, 31.0],
+        }
+    )
+    real_data = pd.DataFrame(
+        {
+            'feature1': [1.5],
+            'target': [15.0],
+            'prediction': [16.0],
+        }
+    )
+
+    result = rce_drift(reference_data, real_data, 'target')
+
+    assert isinstance(result, pd.Series)
+    assert len(result) == 1
+
+
+def test_rce_drift_with_prediction_column():
+    """Test rce_drift using prediction column (drops target)."""
+    reference_data = pd.DataFrame(
+        {
+            'feature1': [1.0, 2.0, 3.0],
+            'target': [10.0, 20.0, 30.0],
+            'prediction': [11.0, 21.0, 31.0],
+        }
+    )
+    real_data = pd.DataFrame(
+        {
+            'feature1': [1.5],
+            'target': [15.0],
+            'prediction': [16.0],
+        }
+    )
+
+    result = rce_drift(reference_data, real_data, 'prediction')
+
+    assert isinstance(result, pd.Series)
+    assert len(result) == 1
+
+
+def test_rce_drift_normalized_output():
+    """Test rce_drift returns normalized distances."""
+    reference_data = pd.DataFrame(
+        {
+            'feature1': [1.0, 2.0, 3.0, 4.0, 5.0],
+            'target': [10.0, 20.0, 30.0, 40.0, 50.0],
+            'prediction': [10.0, 20.0, 30.0, 40.0, 50.0],
+        }
+    )
+    real_data = pd.DataFrame(
+        {
+            'feature1': [2.5, 3.5],
+            'target': [25.0, 35.0],
+            'prediction': [25.0, 35.0],
+        }
+    )
+
+    result = rce_drift(reference_data, real_data, 'target')
+
+    # Result should be a Series with same length as real_data
+    assert isinstance(result, pd.Series)
+    assert len(result) == len(real_data)
+
+
+def test_rce_drift_handles_common_columns():
+    """Test rce_drift correctly handles common columns between datasets."""
+    reference_data = pd.DataFrame(
+        {
+            'feature1': [1.0, 2.0, 3.0],
+            'feature2': [2.0, 3.0, 4.0],
+            'extra_ref': [100.0, 200.0, 300.0],
+            'target': [10.0, 20.0, 30.0],
+            'prediction': [11.0, 21.0, 31.0],
+        }
+    )
+    real_data = pd.DataFrame(
+        {
+            'feature1': [1.5],
+            'feature2': [2.5],
+            'extra_real': [150.0],
+            'target': [15.0],
+            'prediction': [16.0],
+        }
+    )
+
+    result = rce_drift(reference_data, real_data, 'target')
+
+    # Should work with only common columns
+    assert isinstance(result, pd.Series)
+    assert len(result) == 1

tests/sientia/test_reports.py

@@ -0,0 +1,437 @@
+import os
+from unittest.mock import MagicMock
+
+import pytest
+from bs4 import BeautifulSoup
+
+try:
+    from model_manager.sientia import reports
+except ImportError as exc:
+    pytest.skip(
+        f'reports requires Evidently API matching production pin: {exc}',
+        allow_module_level=True,
+    )
+
+
+@pytest.fixture
+def stub_color_options(monkeypatch):
+    def fake_color_options(**kwargs):
+        return dict(kwargs)
+
+    monkeypatch.setattr(reports, 'ColorOptions', fake_color_options)
+
+
+def test_load_html_from_file_success(tmp_path):
+    sample_file = tmp_path / 'sample.html'
+    sample_file.write_text('<p>Hello</p>', encoding='utf-8')
+
+    content = reports.load_html_from_file(str(sample_file))
+
+    assert content == '<p>Hello</p>'
+
+
+def test_load_html_from_file_missing_file():
+    with pytest.raises(FileNotFoundError):
+        reports.load_html_from_file('non-existent.html')
+
+
+def test_load_html_from_file_os_error(monkeypatch):
+    def fake_open(*_args, **_kwargs):
+        raise OSError('boom')
+
+    monkeypatch.setattr('builtins.open', fake_open)
+
+    with pytest.raises(OSError, match='boom'):
+        reports.load_html_from_file('path.html')
+
+
+def test_inject_content_replaces_section():
+    main_html = "<html><body><div id='target'>old</div></body></html>"
+    content = '<span>new</span>'
+
+    result = reports.inject_content(main_html, 'target', content)
+
+    soup = BeautifulSoup(result, 'html.parser')
+    section = soup.find(id='target')
+    assert section is not None
+    assert section.find('span').text == 'new'
+
+
+def test_inject_content_missing_section():
+    main_html = "<html><body><div id='other'>keep</div></body></html>"
+
+    result = reports.inject_content(main_html, 'missing', '<p>ignored</p>')
+
+    # Content should be unchanged when section is missing
+    soup = BeautifulSoup(result, 'html.parser')
+    assert soup.find(id='other') is not None
+    assert soup.find(id='other').text == 'keep'
+
+
+def test_reports_init_sets_defaults(stub_color_options):
+    report = reports.Reports(reference_data='ref', current_data='cur', target_name='target')
+
+    assert report.metrics == []
+    assert isinstance(report.options, list) and len(report.options) == 1
+    assert report.sections == {}
+    assert report.base_path is None
+
+
+def test_add_data_quality_section_without_run(monkeypatch, stub_color_options):
+    monkeypatch.setattr(reports, 'DatasetSummaryMetric', lambda: 'summary')
+    monkeypatch.setattr(
+        reports,
+        'generate_column_metrics',
+        lambda *args, **kwargs: ('columns', kwargs),
+    )
+    monkeypatch.setattr(reports, 'ConflictTargetMetric', lambda: 'conflict')
+    monkeypatch.setattr(reports, 'DatasetCorrelationsMetric', lambda: 'correlations')
+
+    report = reports.Reports(reference_data='ref', current_data='cur', target_name='target')
+    report.add_data_quality_section(columns=['col'], run=False)
+
+    assert report.metrics[-4:] == [
+        'summary',
+        ('columns', {'columns': ['col'], 'skip_id_column': True}),
+        'conflict',
+        'correlations',
+    ]
+    assert 'data_quality' not in report.sections
+
+
+def test_add_data_quality_section_with_run(monkeypatch, tmp_path, stub_color_options):
+    summary = object()
+    column_metrics = object()
+    conflict = object()
+    correlations = object()
+    monkeypatch.setattr(reports, 'DatasetSummaryMetric', lambda: summary)
+
+    def fake_generate_column_metrics(*_args, **kwargs):
+        return column_metrics
+
+    monkeypatch.setattr(reports, 'generate_column_metrics', fake_generate_column_metrics)
+    monkeypatch.setattr(reports, 'ConflictTargetMetric', lambda: conflict)
+    monkeypatch.setattr(reports, 'DatasetCorrelationsMetric', lambda: correlations)
+
+    report_instance = MagicMock()
+    report_instance.as_dict.return_value = {'result': 'data_quality'}
+    ReportMock = MagicMock(return_value=report_instance)
+    monkeypatch.setattr(reports, 'Report', ReportMock)
+
+    report = reports.Reports(
+        reference_data='ref', current_data='cur', target_name='target', base_path=str(tmp_path)
+    )
+    report.add_data_quality_section(columns=['c1'], run=True)
+
+    assert report.metrics[-4:] == [summary, column_metrics, conflict, correlations]
+    assert report.sections['data_quality'] == {'result': 'data_quality'}
+    ReportMock.assert_called_once_with(
+        metrics=[summary, column_metrics, conflict, correlations], options=report.options
+    )
+    run_kwargs = report_instance.run.call_args.kwargs
+    assert run_kwargs['reference_data'] == 'ref'
+    assert run_kwargs['current_data'] == 'cur'
+    assert run_kwargs['column_mapping'].target == 'target'
+    report_instance.save_html.assert_called_once_with(
+        os.path.join(str(tmp_path), 'data_quality.html')
+    )
+
+
+def test_add_data_quality_section_run_without_base_path(monkeypatch, stub_color_options):
+    summary = object()
+    column_metrics = object()
+    conflict = object()
+    correlations = object()
+    monkeypatch.setattr(reports, 'DatasetSummaryMetric', lambda: summary)
+    monkeypatch.setattr(
+        reports,
+        'generate_column_metrics',
+        lambda *args, **kwargs: column_metrics,
+    )
+    monkeypatch.setattr(reports, 'ConflictTargetMetric', lambda: conflict)
+    monkeypatch.setattr(reports, 'DatasetCorrelationsMetric', lambda: correlations)
+
+    report_instance = MagicMock()
+    report_instance.as_dict.return_value = {'result': 'quality'}
+    ReportMock = MagicMock(return_value=report_instance)
+    monkeypatch.setattr(reports, 'Report', ReportMock)
+
+    report = reports.Reports(reference_data='ref', current_data='cur', target_name='target')
+    report.add_data_quality_section(run=True)
+
+    assert report.sections['data_quality'] == {'result': 'quality'}
+    report_instance.save_html.assert_not_called()
+
+
+def test_add_data_quality_section_non_default_target_keeps_conflict_metric(
+    monkeypatch, stub_color_options
+):
+    summary = object()
+    column_metrics = object()
+    conflict = object()
+    correlations = object()
+
+    monkeypatch.setattr(reports, 'DatasetSummaryMetric', lambda: summary)
+    monkeypatch.setattr(
+        reports,
+        'generate_column_metrics',
+        lambda *args, **kwargs: column_metrics,
+    )
+    monkeypatch.setattr(reports, 'ConflictTargetMetric', lambda: conflict)
+    monkeypatch.setattr(reports, 'DatasetCorrelationsMetric', lambda: correlations)
+
+    report = reports.Reports(reference_data='ref', current_data='cur', target_name='sales')
+    report.add_data_quality_section(columns=['c1'], run=False)
+
+    assert report.metrics[-4:] == [
+        summary,
+        column_metrics,
+        conflict,
+        correlations,
+    ]
+
+
+def test_add_data_drift_section_paths(monkeypatch, tmp_path, stub_color_options):
+    drift_instances = [object(), object(), object()]
+    DataDriftPresetMock = MagicMock(side_effect=drift_instances)
+    monkeypatch.setattr(reports, 'DataDriftPreset', DataDriftPresetMock)
+
+    report_instance = MagicMock()
+    report_instance.as_dict.return_value = {'result': 'data_drift'}
+    ReportMock = MagicMock(return_value=report_instance)
+    monkeypatch.setattr(reports, 'Report', ReportMock)
+
+    report = reports.Reports(
+        reference_data='ref', current_data='cur', target_name='target', base_path=str(tmp_path)
+    )
+    report.add_data_drift_section(columns=['c1'], run=False)
+    assert report.metrics[-1] == drift_instances[0]
+    assert 'data_drift' not in report.sections
+
+    report.add_data_drift_section(columns=['c1'], run=True)
+    assert report.sections['data_drift'] == {'result': 'data_drift'}
+    ReportMock.assert_called_with(metrics=[drift_instances[2]], options=report.options)
+    run_kwargs = report_instance.run.call_args.kwargs
+    assert run_kwargs['reference_data'] == 'ref'
+    assert run_kwargs['current_data'] == 'cur'
+    assert run_kwargs['column_mapping'].target == 'target'
+    report_instance.save_html.assert_called_with(os.path.join(str(tmp_path), 'data_drift.html'))
+
+
+def test_add_data_drift_section_run_without_base_path(monkeypatch, stub_color_options):
+    drift_instances = [object(), object(), object()]
+    DataDriftPresetMock = MagicMock(side_effect=drift_instances)
+    monkeypatch.setattr(reports, 'DataDriftPreset', DataDriftPresetMock)
+
+    report_instance = MagicMock()
+    report_instance.as_dict.return_value = {'result': 'drift'}
+    ReportMock = MagicMock(return_value=report_instance)
+    monkeypatch.setattr(reports, 'Report', ReportMock)
+
+    report = reports.Reports(reference_data='ref', current_data='cur', target_name='target')
+    report.add_data_drift_section(run=True)
+
+    assert report.sections['data_drift'] == {'result': 'drift'}
+    report_instance.save_html.assert_not_called()
+
+
+def test_add_regression_section(monkeypatch, tmp_path, stub_color_options):
+    regression_metrics = [object() for _ in range(7)]
+    monkeypatch.setattr(reports, 'RegressionPerformanceMetrics', lambda: regression_metrics[0])
+    monkeypatch.setattr(reports, 'RegressionDummyMetric', lambda: regression_metrics[1])
+    monkeypatch.setattr(
+        reports, 'RegressionPredictedVsActualScatter', lambda: regression_metrics[2]
+    )
+    monkeypatch.setattr(reports, 'RegressionPredictedVsActualPlot', lambda: regression_metrics[3])
+    monkeypatch.setattr(reports, 'RegressionErrorPlot', lambda: regression_metrics[4])
+    monkeypatch.setattr(reports, 'RegressionAbsPercentageErrorPlot', lambda: regression_metrics[5])
+    monkeypatch.setattr(reports, 'RegressionErrorDistribution', lambda: regression_metrics[6])
+
+    report_instance = MagicMock()
+    report_instance.as_dict.return_value = {'result': 'regression'}
+    ReportMock = MagicMock(return_value=report_instance)
+    monkeypatch.setattr(reports, 'Report', ReportMock)
+
+    report = reports.Reports(
+        reference_data='ref', current_data='cur', target_name='target', base_path=str(tmp_path)
+    )
+
+    report.add_regression_section(run=False)
+    assert report.metrics[-7:] == regression_metrics
+    assert 'regression' not in report.sections
+
+    report.add_regression_section(run=True)
+    assert report.sections['regression'] == {'result': 'regression'}
+    ReportMock.assert_called_with(metrics=regression_metrics, options=report.options)
+    report_instance.run.assert_called_with(
+        reference_data='ref',
+        current_data='cur',
+        column_mapping=report_instance.run.call_args.kwargs['column_mapping'],
+    )
+    report_instance.save_html.assert_called_with(os.path.join(str(tmp_path), 'regression.html'))
+
+
+def test_add_regression_section_run_without_base_path(monkeypatch, stub_color_options):
+    regression_metrics = [object() for _ in range(7)]
+    monkeypatch.setattr(reports, 'RegressionPerformanceMetrics', lambda: regression_metrics[0])
+    monkeypatch.setattr(reports, 'RegressionDummyMetric', lambda: regression_metrics[1])
+    monkeypatch.setattr(
+        reports, 'RegressionPredictedVsActualScatter', lambda: regression_metrics[2]
+    )
+    monkeypatch.setattr(reports, 'RegressionPredictedVsActualPlot', lambda: regression_metrics[3])
+    monkeypatch.setattr(reports, 'RegressionErrorPlot', lambda: regression_metrics[4])
+    monkeypatch.setattr(reports, 'RegressionAbsPercentageErrorPlot', lambda: regression_metrics[5])
+    monkeypatch.setattr(reports, 'RegressionErrorDistribution', lambda: regression_metrics[6])
+
+    report_instance = MagicMock()
+    report_instance.as_dict.return_value = {'result': 'reg'}
+    ReportMock = MagicMock(return_value=report_instance)
+    monkeypatch.setattr(reports, 'Report', ReportMock)
+
+    report = reports.Reports(reference_data='ref', current_data='cur', target_name='target')
+    report.add_regression_section(run=True)
+
+    assert report.sections['regression'] == {'result': 'reg'}
+    report_instance.save_html.assert_not_called()
+
+
+def test_set_color_options_appends(monkeypatch):
+    calls = []
+
+    def color_options_mock(**kwargs):
+        calls.append(kwargs)
+        return kwargs
+
+    monkeypatch.setattr(reports, 'ColorOptions', color_options_mock)
+
+    report = reports.Reports(reference_data='ref', current_data='cur', target_name='target')
+    report.set_color_options(primary_color='#111', secondary_color='#222')
+
+    options = report.options
+    assert options is not None
+    assert len(options) == 2
+    assert calls[0]['primary_color'] == '#0F4C81'
+    assert calls[1]['primary_color'] == '#111'
+    assert options[1]['secondary_color'] == '#222'
+
+
+def test_save_all_sections_html_requires_base_path(stub_color_options):
+    report = reports.Reports(reference_data='ref', current_data='cur', target_name='target')
+
+    with pytest.raises(ValueError):
+        report.save_all_sections_html('output/report.html')
+
+
+def test_save_all_sections_html_requires_template_path(stub_color_options, tmp_path):
+    report = reports.Reports(
+        reference_data='ref',
+        current_data='cur',
+        target_name='target',
+        base_path=str(tmp_path),
+    )
+
+    with pytest.raises(ValueError, match='template_path is required'):
+        report.save_all_sections_html('output/report.html')
+
+
+def test_save_all_sections_html_writes_output(tmp_path, stub_color_options):
+    base_dir = tmp_path / 'templates'
+    base_dir.mkdir()
+    (base_dir / 'header.html').write_text(
+        "<html><body><div id='data_drift'></div><div id='data_quality'></div><div id='regression'></div></body></html>",
+        encoding='utf-8',
+    )
+    (base_dir / 'data_drift.html').write_text('<p>Drift</p>', encoding='utf-8')
+    (base_dir / 'data_quality.html').write_text('<p>Quality</p>', encoding='utf-8')
+    (base_dir / 'regression.html').write_text('<p>Regression</p>', encoding='utf-8')
+
+    report = reports.Reports(
+        reference_data='ref',
+        current_data='cur',
+        target_name='target',
+        base_path=str(base_dir),
+        template_path=str(base_dir),
+    )
+    output_path = tmp_path / 'reports' / 'combined.html'
+
+    report.save_all_sections_html(str(output_path))
+
+    assert output_path.exists()
+    content = output_path.read_text(encoding='utf-8')
+    assert '<p>Drift</p>' in content
+    assert '<p>Quality</p>' in content
+    assert '<p>Regression</p>' in content
+
+
+def test_save_all_sections_html_creates_directory(monkeypatch, tmp_path, stub_color_options):
+    base_dir = tmp_path / 'templates'
+    base_dir.mkdir()
+    (base_dir / 'header.html').write_text(
+        "<html><body><div id='data_drift'></div><div id='data_quality'></div><div id='regression'></div></body></html>",
+        encoding='utf-8',
+    )
+    (base_dir / 'data_drift.html').write_text('<p>Drift</p>', encoding='utf-8')
+    (base_dir / 'data_quality.html').write_text('<p>Quality</p>', encoding='utf-8')
+    (base_dir / 'regression.html').write_text('<p>Regression</p>', encoding='utf-8')
+
+    make_dirs_called = []
+    report = reports.Reports(
+        reference_data='ref',
+        current_data='cur',
+        target_name='target',
+        base_path=str(base_dir),
+        template_path=str(base_dir),
+    )
+    output_path = tmp_path / 'nested' / 'report.html'
+    output_dir = str(output_path.parent)
+
+    original_exists = os.path.exists
+    original_makedirs = os.makedirs
+
+    def fake_exists(path):
+        if path == output_dir:
+            return False
+        return original_exists(path)
+
+    def fake_makedirs(path, exist_ok=False):
+        make_dirs_called.append((path, exist_ok))
+        return original_makedirs(path, exist_ok=exist_ok)
+
+    monkeypatch.setattr(os.path, 'exists', fake_exists)
+    monkeypatch.setattr(os, 'makedirs', fake_makedirs)
+
+    report.save_all_sections_html(str(output_path))
+
+    assert make_dirs_called == [(str(output_path.parent), True)]
+
+
+def test_save_all_sections_html_no_directory_needed(monkeypatch, tmp_path, stub_color_options):
+    base_dir = tmp_path / 'templates'
+    base_dir.mkdir()
+    (base_dir / 'header.html').write_text(
+        "<html><body><div id='data_drift'></div><div id='data_quality'></div><div id='regression'></div></body></html>",
+        encoding='utf-8',
+    )
+    (base_dir / 'data_drift.html').write_text('<p>Drift</p>', encoding='utf-8')
+    (base_dir / 'data_quality.html').write_text('<p>Quality</p>', encoding='utf-8')
+    (base_dir / 'regression.html').write_text('<p>Regression</p>', encoding='utf-8')
+
+    mk_calls = []
+
+    def fake_makedirs(path, exist_ok=False):
+        mk_calls.append((path, exist_ok))
+
+    monkeypatch.setattr(os, 'makedirs', fake_makedirs)
+    monkeypatch.chdir(tmp_path)
+
+    report = reports.Reports(
+        reference_data='ref',
+        current_data='cur',
+        target_name='target',
+        base_path=str(base_dir),
+        template_path=str(base_dir),
+    )
+    report.save_all_sections_html('report.html')
+
+    assert mk_calls == []
+    assert (tmp_path / 'report.html').exists()

tests/test_metrics.py

@@ -0,0 +1,379 @@
+"""Unit tests for model_manager.metrics module.
+
+This module tests the Prometheus metrics configuration used for
+monitoring and observability in the Sientia DataOps Model Manager.
+"""
+
+
+def test_app_up_metric_exists():
+    """Test that APP_UP metric is properly defined."""
+    from model_manager.metrics import APP_UP
+
+    assert APP_UP is not None
+    assert APP_UP._name == 'app_up'
+    assert (
+        APP_UP._documentation == 'Indicates if the application is running (1) or shutting down (0)'
+    )
+
+
+def test_app_up_metric_has_pod_id_label():
+    """Test that APP_UP metric has pod_id label."""
+    from model_manager.metrics import APP_UP
+
+    assert 'pod_id' in APP_UP._labelnames
+
+
+def test_app_up_metric_is_gauge():
+    """Test that APP_UP is a Gauge metric."""
+    from prometheus_client import Gauge
+
+    from model_manager.metrics import APP_UP
+
+    assert isinstance(APP_UP, Gauge)
+
+
+def test_app_up_metric_can_be_set_to_one():
+    """Test that APP_UP metric can be set to 1 (running)."""
+    from model_manager.metrics import APP_UP
+
+    # Set metric to 1 for a specific pod
+    APP_UP.labels(pod_id='test-pod-1').set(1)
+
+    # Verify the metric value
+    metric_value = APP_UP.labels(pod_id='test-pod-1')._value._value
+    assert metric_value == 1
+
+
+def test_app_up_metric_can_be_set_to_zero():
+    """Test that APP_UP metric can be set to 0 (shutting down)."""
+    from model_manager.metrics import APP_UP
+
+    # Set metric to 0 for a specific pod
+    APP_UP.labels(pod_id='test-pod-2').set(0)
+
+    # Verify the metric value
+    metric_value = APP_UP.labels(pod_id='test-pod-2')._value._value
+    assert metric_value == 0
+
+
+def test_app_up_metric_multiple_pods():
+    """Test that APP_UP metric can track multiple pods independently."""
+    from model_manager.metrics import APP_UP
+
+    # Set different values for different pods
+    APP_UP.labels(pod_id='pod-1').set(1)
+    APP_UP.labels(pod_id='pod-2').set(0)
+    APP_UP.labels(pod_id='pod-3').set(1)
+
+    # Verify each pod has correct value
+    assert APP_UP.labels(pod_id='pod-1')._value._value == 1
+    assert APP_UP.labels(pod_id='pod-2')._value._value == 0
+    assert APP_UP.labels(pod_id='pod-3')._value._value == 1
+
+
+def test_app_up_metric_default_value():
+    """Test that APP_UP metric starts with no value set."""
+    # Create a new label that hasn't been used yet
+    import uuid
+
+    from model_manager.metrics import APP_UP
+
+    unique_pod = f'test-pod-{uuid.uuid4()}'
+
+    # The metric should exist but not have a value until set
+    metric = APP_UP.labels(pod_id=unique_pod)
+    assert metric is not None
+
+
+def test_metrics_module_imports():
+    """Test that metrics module can be imported successfully."""
+    import model_manager.metrics
+
+    assert hasattr(model_manager.metrics, 'APP_UP')
+    assert hasattr(model_manager.metrics, 'Gauge')
+
+
+def test_metrics_module_docstring():
+    """Test that metrics module has proper documentation."""
+    import model_manager.metrics
+
+    assert model_manager.metrics.__doc__ is not None
+    assert 'Prometheus' in model_manager.metrics.__doc__
+    assert 'metrics' in model_manager.metrics.__doc__
+
+
+def test_app_up_metric_can_increment():
+    """Test that APP_UP metric value can be incremented."""
+    from model_manager.metrics import APP_UP
+
+    pod_id = 'test-pod-increment'
+    APP_UP.labels(pod_id=pod_id).set(0)
+
+    # Increment the metric
+    APP_UP.labels(pod_id=pod_id).inc()
+
+    metric_value = APP_UP.labels(pod_id=pod_id)._value._value
+    assert metric_value == 1
+
+
+def test_app_up_metric_can_decrement():
+    """Test that APP_UP metric value can be decremented."""
+    from model_manager.metrics import APP_UP
+
+    pod_id = 'test-pod-decrement'
+    APP_UP.labels(pod_id=pod_id).set(1)
+
+    # Decrement the metric
+    APP_UP.labels(pod_id=pod_id).dec()
+
+    metric_value = APP_UP.labels(pod_id=pod_id)._value._value
+    assert metric_value == 0
+
+
+def test_app_up_metric_set_to_timestamp():
+    """Test that APP_UP metric can be set to current timestamp."""
+    import time
+
+    from model_manager.metrics import APP_UP
+
+    pod_id = 'test-pod-timestamp'
+    current_time = time.time()
+
+    # Set to timestamp
+    APP_UP.labels(pod_id=pod_id).set_to_current_time()
+
+    metric_value = APP_UP.labels(pod_id=pod_id)._value._value
+
+    # Should be close to current time
+    assert abs(metric_value - current_time) < 2  # Within 2 seconds
+
+
+def test_app_up_metric_label_validation():
+    """Test that APP_UP metric validates label names."""
+    from model_manager.metrics import APP_UP
+
+    # Should work with valid label
+    APP_UP.labels(pod_id='valid-pod-name').set(1)
+
+    # Should work with empty string (though not recommended)
+    APP_UP.labels(pod_id='').set(1)
+
+    # Should work with special characters
+    APP_UP.labels(pod_id='pod-123_test.example').set(1)
+
+
+def test_module_exports():
+    """Test that metrics module exports expected symbols."""
+    import model_manager.metrics as metrics_module
+
+    # Check that module has the expected exports
+    module_contents = dir(metrics_module)
+
+    assert 'APP_UP' in module_contents
+    assert 'Gauge' in module_contents
+
+
+def test_app_up_metric_thread_safety():
+    """Test that APP_UP metric is thread-safe."""
+    import threading
+
+    from model_manager.metrics import APP_UP
+
+    pod_id = 'test-pod-threading'
+    APP_UP.labels(pod_id=pod_id).set(0)
+
+    def increment_metric():
+        for _ in range(100):
+            APP_UP.labels(pod_id=pod_id).inc()
+
+    # Create multiple threads that increment the metric
+    threads = [threading.Thread(target=increment_metric) for _ in range(5)]
+
+    for thread in threads:
+        thread.start()
+
+    for thread in threads:
+        thread.join()
+
+    # Should have incremented 500 times total
+    metric_value = APP_UP.labels(pod_id=pod_id)._value._value
+    assert metric_value == 500
+
+
+def test_prometheus_client_gauge_import():
+    """Test that Gauge is properly imported from prometheus_client."""
+    from prometheus_client import Gauge as PrometheusGauge
+
+    from model_manager.metrics import Gauge
+
+    assert Gauge is PrometheusGauge
+
+
+# ---------------------------------------------------------------------------
+# Training metrics — existence, type, and labels
+# ---------------------------------------------------------------------------
+
+_TRAINING_LABEL_NAMES = ('pod_id', 'model_name', 'model_type')
+
+
+def _assert_training_labels(metric):
+    for label in _TRAINING_LABEL_NAMES:
+        assert label in metric._labelnames
+
+
+def test_sientia_training_data_preparation_lag_is_histogram():
+    from prometheus_client import Histogram
+
+    from model_manager.metrics import SIENTIA_TRAINING_DATA_PREPARATION_LAG
+
+    assert isinstance(SIENTIA_TRAINING_DATA_PREPARATION_LAG, Histogram)
+    assert SIENTIA_TRAINING_DATA_PREPARATION_LAG._name == 'sientia_training_data_preparation_lag'
+    _assert_training_labels(SIENTIA_TRAINING_DATA_PREPARATION_LAG)
+
+
+def test_sientia_training_data_preparation_error_count_total_is_counter():
+    from prometheus_client import Counter
+
+    from model_manager.metrics import SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL
+
+    assert isinstance(SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL, Counter)
+    assert 'sientia_training_data_preparation_error_count' in SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL._name
+    _assert_training_labels(SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL)
+
+
+def test_sientia_training_model_fit_lag_is_histogram():
+    from prometheus_client import Histogram
+
+    from model_manager.metrics import SIENTIA_TRAINING_MODEL_FIT_LAG
+
+    assert isinstance(SIENTIA_TRAINING_MODEL_FIT_LAG, Histogram)
+    assert SIENTIA_TRAINING_MODEL_FIT_LAG._name == 'sientia_training_model_fit_lag'
+    _assert_training_labels(SIENTIA_TRAINING_MODEL_FIT_LAG)
+
+
+def test_sientia_training_model_fit_error_count_total_is_counter():
+    from prometheus_client import Counter
+
+    from model_manager.metrics import SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL
+
+    assert isinstance(SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL, Counter)
+    assert 'sientia_training_model_fit_error_count' in SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL._name
+    _assert_training_labels(SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL)
+
+
+def test_sientia_training_model_quality_mse_is_gauge():
+    from prometheus_client import Gauge
+
+    from model_manager.metrics import SIENTIA_TRAINING_MODEL_QUALITY_MSE
+
+    assert isinstance(SIENTIA_TRAINING_MODEL_QUALITY_MSE, Gauge)
+    assert SIENTIA_TRAINING_MODEL_QUALITY_MSE._name == 'sientia_training_model_quality_mse'
+    _assert_training_labels(SIENTIA_TRAINING_MODEL_QUALITY_MSE)
+
+
+def test_sientia_training_model_quality_mae_is_gauge():
+    from prometheus_client import Gauge
+
+    from model_manager.metrics import SIENTIA_TRAINING_MODEL_QUALITY_MAE
+
+    assert isinstance(SIENTIA_TRAINING_MODEL_QUALITY_MAE, Gauge)
+    assert SIENTIA_TRAINING_MODEL_QUALITY_MAE._name == 'sientia_training_model_quality_mae'
+    _assert_training_labels(SIENTIA_TRAINING_MODEL_QUALITY_MAE)
+
+
+def test_sientia_training_model_quality_r2_is_gauge():
+    from prometheus_client import Gauge
+
+    from model_manager.metrics import SIENTIA_TRAINING_MODEL_QUALITY_R2
+
+    assert isinstance(SIENTIA_TRAINING_MODEL_QUALITY_R2, Gauge)
+    assert SIENTIA_TRAINING_MODEL_QUALITY_R2._name == 'sientia_training_model_quality_r2'
+    _assert_training_labels(SIENTIA_TRAINING_MODEL_QUALITY_R2)
+
+
+def test_sientia_training_dataset_train_rows_is_gauge():
+    from prometheus_client import Gauge
+
+    from model_manager.metrics import SIENTIA_TRAINING_DATASET_TRAIN_ROWS
+
+    assert isinstance(SIENTIA_TRAINING_DATASET_TRAIN_ROWS, Gauge)
+    assert SIENTIA_TRAINING_DATASET_TRAIN_ROWS._name == 'sientia_training_dataset_train_rows'
+    _assert_training_labels(SIENTIA_TRAINING_DATASET_TRAIN_ROWS)
+
+
+def test_sientia_training_dataset_val_rows_is_gauge():
+    from prometheus_client import Gauge
+
+    from model_manager.metrics import SIENTIA_TRAINING_DATASET_VAL_ROWS
+
+    assert isinstance(SIENTIA_TRAINING_DATASET_VAL_ROWS, Gauge)
+    assert SIENTIA_TRAINING_DATASET_VAL_ROWS._name == 'sientia_training_dataset_val_rows'
+    _assert_training_labels(SIENTIA_TRAINING_DATASET_VAL_ROWS)
+
+
+def test_sientia_training_model_trained_total_is_counter():
+    from prometheus_client import Counter
+
+    from model_manager.metrics import SIENTIA_TRAINING_MODEL_TRAINED_TOTAL
+
+    assert isinstance(SIENTIA_TRAINING_MODEL_TRAINED_TOTAL, Counter)
+    assert 'sientia_training_model_trained' in SIENTIA_TRAINING_MODEL_TRAINED_TOTAL._name
+    _assert_training_labels(SIENTIA_TRAINING_MODEL_TRAINED_TOTAL)
+
+
+def test_sientia_training_feature_count_is_gauge():
+    from prometheus_client import Gauge
+
+    from model_manager.metrics import SIENTIA_TRAINING_FEATURE_COUNT
+
+    assert isinstance(SIENTIA_TRAINING_FEATURE_COUNT, Gauge)
+    assert SIENTIA_TRAINING_FEATURE_COUNT._name == 'sientia_training_feature_count'
+    _assert_training_labels(SIENTIA_TRAINING_FEATURE_COUNT)
+
+
+def test_sientia_training_info_is_gauge():
+    from prometheus_client import Gauge
+
+    from model_manager.metrics import SIENTIA_TRAINING_INFO
+
+    assert isinstance(SIENTIA_TRAINING_INFO, Gauge)
+    assert SIENTIA_TRAINING_INFO._name == 'sientia_training_info'
+
+    expected_labels = {
+        'pod_id', 'model_name', 'model_type',
+        'dataset_train_rows', 'dataset_val_rows', 'feature_count',
+        'mse', 'mae', 'r2',
+    }
+    assert expected_labels == set(SIENTIA_TRAINING_INFO._labelnames)
+
+
+def test_sientia_training_info_set_value():
+    import time
+
+    from model_manager.metrics import SIENTIA_TRAINING_INFO
+
+    ts = time.time() * 1000
+    SIENTIA_TRAINING_INFO.labels(
+        pod_id='test-pod',
+        model_name='my_model',
+        model_type='linear',
+        dataset_train_rows='1000',
+        dataset_val_rows='200',
+        feature_count='5',
+        mse='0.01',
+        mae='0.08',
+        r2='0.95',
+    ).set(ts)
+
+    value = SIENTIA_TRAINING_INFO.labels(
+        pod_id='test-pod',
+        model_name='my_model',
+        model_type='linear',
+        dataset_train_rows='1000',
+        dataset_val_rows='200',
+        feature_count='5',
+        mse='0.01',
+        mae='0.08',
+        r2='0.95',
+    )._value._value
+    assert abs(value - ts) < 2000