Sientia DataOps Model Manager

A comprehensive AI model management platform for the complete machine learning lifecycle. Handles model training, versioning, deployment, monitoring, and governance. Streamlines MLOps workflows with centralized model registry, automated pipelines, performance tracking, and enterprise-grade compliance features.

Features

Core Functionality

• Batch Prediction Processing: High-throughput ML model inference using MLFlow models
• Temporal Workflow Orchestration: Robust workflow management with automatic retry policies and fault tolerance
• Data Quality Gates: Configurable filtering for data validation, MLFlow API responses, and custom validation rules
• Multi-Model Support: Flexible ML model management with retention policies and versioning
• Real-time Data Export: PostgreSQL persistence for data storage
• Comprehensive Monitoring: Prometheus metrics and detailed logging for operational visibility

Advanced Capabilities

• Incremental Data Processing: Timestamp-based data loading to avoid reprocessing
• Configurable Data Retention: Model retention policies with automatic cleanup
• Notification System: Integrated alerting and notification management via MongoDB
• Scalable Architecture: Kubernetes-ready deployment with horizontal scaling support
• Model Retraining: Automated model retraining workflows with production model updates

Development & Quality Assurance

• Code Quality Tools: Ruff (linting/formatting), mypy (type checking), Bandit (security analysis)
• Automated Validation: Pre-commit validation script and CI/CD integration
• Comprehensive Testing: pytest with async support and 70%+ code coverage
• Type Safety: Static type checking with mypy for improved code reliability

Architecture

The Model Manager system uses a Temporal-based workflow architecture with clear separation of concerns and robust error handling. The architecture is designed for high availability, scalability, and operational excellence in production ML environments.

Architecture Principles

1. Separation of Concerns

• Worker Layer: Manages Temporal workers, task queues, and application lifecycle
• Workflow Layer: Orchestrates business logic and process coordination
• Activity Layer: Implements specific operations and external system interactions
• Data Layer: Handles data persistence, caching, and external service connections

2. Fault Tolerance & Resilience

• Automatic Retry Policies: Configurable retry strategies for transient failures
• Circuit Breaker Pattern: Prevents cascading failures in external service calls
• Graceful Degradation: System continues operating with reduced functionality
• Comprehensive Error Handling: Detailed error reporting and notification integration

3. Scalability & Performance

• Horizontal Scaling: Multiple worker instances for load distribution
• Task Queue Isolation: Separate queues for different workflow types
• Connection Pooling: Optimized database and external service connections
• Asynchronous Processing: Non-blocking operations for improved throughput

4. Observability & Monitoring

• Prometheus Metrics: Comprehensive system and business metrics
• Structured Logging: Consistent log format with correlation IDs
• Health Checks: Endpoint health monitoring and alerting
• Performance Tracing: Request flow tracking and bottleneck identification

Key Components

Worker (model_manager/worker/worker.py)

• Purpose: Main application orchestrator managing Temporal workers and task queues
• Responsibilities:
  • Temporal client initialization and connection management
  • Worker lifecycle management and graceful shutdown
  • Task queue configuration and load balancing
  • Prometheus metrics server initialization
  • Notification handler setup and configuration
• Key Features:
  • Automatic scaling with PollerBehaviorAutoscaling
  • Health check endpoints for Kubernetes liveness/readiness probes
  • Graceful shutdown with cleanup procedures
  • Multi-instance deployment support
  • Two dedicated task queues: predictions_batch-queue and minimal_retrain-queue

Workflows (model_manager/workflows/)

• PredictionsBatch: Main entry point for batch prediction pipelines
• PredictionProcess: Core prediction pipeline with MLFlow integration
• FormatAndExportPrediction: Data formatting and export operations
• MinimalRetrain: Automated model retraining and deployment
• Key Features:
  • Temporal workflow definitions with retry policies
  • Child workflow orchestration and delegation
  • Comprehensive error handling and recovery
  • Configurable timeout and retry strategies

Activities (model_manager/activities/)

• Activities: Main activity orchestrator combining all functionality through multiple inheritance
• Gates: Data quality validation and filtering mechanisms
• MLFlow: Model transformation and prediction operations
• Key Features:
  • Multiple inheritance pattern for unified activity interface
  • Configurable filter policies and validation rules
  • MLFlow model serving integration with configurable flavors
  • Comprehensive error handling and notification integration

Data Services (model_manager/utils/)

• Connectors Config: Environment variable-based configuration management
• Repository: Data access layer for MLFlow operations
  • model_repository.py: MLFlow model operations and retraining
• Filters: Data quality validation and MLFlow response filtering
  • conditional_filters.py: Input data validation filters
  • mlflow_filters.py: MLFlow API response validation filters
• Key Features:
  • Environment variable-based configuration with sensible defaults
  • Connection pool management and optimization
  • Security credential management
  • Configuration validation and error handling
  • Support for MLFlow model flavors

Data Flow Architecture

1. Batch Prediction Pipeline

Input Data (PostgreSQL) → Data Quality Gates → MLFlow Transform → 
MLFlow Prediction → Response Validation → Export (PostgreSQL)

2. Model Retraining Pipeline

Training Data → Model Retraining → Quality Validation → 
Production Update → Notification & Monitoring

Security Architecture

Authentication & Authorization

• MLFlow API Authentication: Username/password with secure transmission
• Database Connection Security: Encrypted connections with credential management
• Kubernetes Secrets Integration: Secure credential storage and access

Network Security

• TLS/SSL Encryption: Secure communication channels
• Network Isolation: Kubernetes network policies and service mesh
• Firewall Rules: Controlled access to external services
• VPN Integration: Secure remote access and management

Data Security

• Data Encryption: At-rest and in-transit encryption
• Access Control: Role-based access control (RBAC)
• Audit Logging: Comprehensive access and operation logging
• Data Retention: Configurable data lifecycle management

🔄 Workflows

1. Predictions Batch Workflow (predictions_batch.py)

The PredictionsBatch workflow is the main entry point for batch prediction pipelines. It orchestrates the complete prediction process and implements a robust data loading and processing pattern.

Purpose

• Batch Prediction Orchestration: Coordinates data loading and prediction processing
• Data Preparation: Loads data using custom SQL queries with configurable schemas
• Workflow Delegation: Delegates actual prediction processing to the PredictionProcess workflow
• Configuration Management: Handles model configuration, filters, and retention policies

Execution Flow

1. Data Loading: Executes custom SQL query to load data from PostgreSQL
2. Input Preparation: Prepares prediction input with metadata and configuration
3. Workflow Delegation: Spawns PredictionProcess child workflow for actual processing
4. Error Handling: Implements comprehensive error handling with retry policies

Key Features

• Custom Query Support: Flexible SQL-based data loading
• Schema Configuration: Configurable data schema definitions
• Automatic Retry: Implements Temporal retry policies for fault tolerance
• Timeout Management: 60-second timeout for all activities
• Comprehensive Error Handling: Detailed error reporting and notification integration

Input Parameters

{
  "schedule_name": "hourly_predictions",
  "model_name": "temperature_prediction_model",
  "model_id": "temp_pred_001",
  "query": "SELECT * FROM sensor_data WHERE timestamp > NOW() - INTERVAL '1 hour'",
  "schema": {
    "timestamp": "datetime",
    "temperature": "float",
    "humidity": "float"
  },
  "table_name": "predictions",
  "input_filters": {
    "EMPTY_DATA": {"POLICY": "STOP"}
  },
  "mlflow_transform_filters": {
    "API_ERROR": {"POLICY": "STOP"}
  },
  "mlflow_predict_filters": {
    "API_ERROR": {"POLICY": "STOP"}
  },
  "model_retention": 60,
  "path_priority": ["STOP", "CONTINUE", "REPEAT"]
}

Architecture Diagram

flowchart LR
    A[1. load_custom_query] --> B[2. prediction_process 🔃]
    
    A -.-> Database[(Database)]

2. Prediction Process Workflow (prediction_process.py)

The PredictionProcess workflow implements the core prediction pipeline for ML model inference. It handles data quality validation, MLFlow model interactions, and prediction processing.

Purpose

• Data Quality Validation: Applies configurable filters for data integrity
• MLFlow Integration: Manages model transformation and prediction requests
• Response Validation: Filters MLFlow API responses for quality assurance
• Prediction Export: Delegates prediction formatting and export operations

Execution Flow

1. Timestamp Retrieval: Gets the last processed timestamp for incremental processing
2. Input Data Gate: Applies configured filters for data quality validation
3. Path Decision: Determines processing path based on filter results
4. MLFlow Transform: Requests data transformation using MLFlow models
5. Response Validation: Filters transform responses for quality assurance
6. MLFlow Prediction: Executes prediction using transformed data
7. Content Validation: Filters prediction responses for final quality check
8. Export Delegation: Delegates to FormatAndExportPrediction workflow

Key Features

• Configurable Quality Gates: Multiple filter types with policy-based configuration
• Flexible Path Handling: Configurable decision paths (STOP, CONTINUE, REPEAT)
• MLFlow Integration: Comprehensive model management and inference
• Incremental Processing: Timestamp-based data processing optimization
• Comprehensive Monitoring: Detailed metrics and error reporting

Input Parameters

{
  "metadata": {
    "schedule_name": "hourly_predictions",
    "model_name": "temperature_prediction_model",
    "model_id": "temp_pred_001",
    "workflow_name": "predictions_batch"
  },
  "data": {...},
  "schema": {...},
  "table_name": "predictions",
  "model_id": "temp_pred_001",
  "model_name": "temperature_prediction_model",
  "input_filters": {
    "EMPTY_DATA": {"POLICY": "STOP"},
    "SPECIFIC_VARIABLES_NULL_VALUES": {
      "POLICY": "STOP",
      "config": {"variables": ["temperature", "humidity"]}
    }
  },
  "mlflow_transform_filters": {
    "API_ERROR": {"POLICY": "STOP"}
  },
  "mlflow_predict_filters": {
    "API_ERROR": {"POLICY": "STOP"},
    "NAN_VALUES": {"POLICY": "STOP"}
  },
  "model_retention": 60,
  "path_priority": ["STOP", "CONTINUE", "REPEAT"]
}

Architecture Diagram

flowchart LR
    A[1. get_last_timestamp] --> B[2. input_gate] --> C[3. request_transform] --> D[4. mlflow_response_gate] --> E[5. mlflow_content_gate] --> F[6. request_predict] --> G[7. mlflow_response_gate] --> H[8. format_and_export_prediction🔃]
    
    A -.-> Redis[(Redis)]
    C -.-> MLFlow[MLFlow]
    F -.-> MLFlow[MLFlow]
    G -.-> Filters[MLFlow Filters]

3. Format and Export Prediction Workflow (format_and_export_prediction.py)

The FormatAndExportPrediction workflow handles prediction data formatting and export operations to multiple destinations.

Purpose

• Data Formatting: Formats prediction data for database storage
• PostgreSQL Export: Persists predictions to database with metrics
• Metrics Recording: Tracks export operations and performance metrics

Execution Flow

1. Path Decision: Determines formatting path based on configuration
2. Data Formatting: Formats prediction data for specific output requirements
3. PostgreSQL Export: Writes formatted predictions to database
4. Metrics Recording: Records export performance and success metrics

Key Features

• Flexible Formatting: Configurable output formats for different destinations
• Database Export: PostgreSQL integration for data persistence
• Performance Monitoring: Comprehensive metrics for export operations
• Error Handling: Robust error handling with notification integration

Architecture Diagram

flowchart LR
    A[1. format_prediction/format_default_prediction] --> B[2. export_data_to_postgres] --> C[3. write_metrics]
    
    A -.-> Format[Data Formatting]
    B -.-> PostgreSQL[(PostgreSQL)]
    C -.-> Prometheus[Prometheus]

4. Minimal Retrain Workflow (minimal_retrain.py)

The MinimalRetrain workflow handles automated model retraining and production model updates.

Purpose

• Model Retraining: Automates ML model retraining processes
• Production Updates: Manages production model version updates
• Data Export: Exports training data for model development
• Quality Assurance: Ensures model quality before production deployment

Execution Flow

1. Data Loading: Loads training data using custom queries
2. Model Retraining: Executes model retraining process
3. Quality Validation: Validates retrained model performance
4. Production Update: Updates production model if quality criteria met
5. Data Export: Exports training data for analysis

Architecture Diagram

flowchart LR
    A[1. load_custom_query] --> B[2. retrain_model] --> C[3. update_production_model] --> D[4. export_data_to_postgres]
    
    A -.-> Database[(Database)]
    B -.-> MLFlow[MLFlow]
    C -.-> MLFlow[MLFlow]
    D -.-> PostgreSQL[(PostgreSQL)]

📋 Prerequisites

• Python 3.11+
• Temporal server/cluster
• PostgreSQL database
• MLFlow server
• MongoDB server (for notifications)

Note: External dependencies must be available either through:

• Kubernetes cluster deployment
• Docker Compose setup
• Cloud-managed services
• Local installations

🚀 Installation

Local Development Setup

1. Clone the repository

   git clone <repository-url>
   cd sientia-dataops-model-manager
   

2. Create virtual environment

   conda create -p ./venv python=3.11
   conda activate ./venv
   

3. Install dependencies

4. Install github cli bash sudo apt update sudo apt install gh -y

5. Authenticate with github bash gh auth login

6. Run the install_dependencies.sh script bash chmod +x install_dependencies.sh ./install_dependencies.sh

7. Install Python dependencies bash python -m pip install --upgrade pip # Install production dependencies pip install -r requirements.txt # Install development and testing tools pip install -r requirements-dev.txt

8. Create environment configuration file

   cp .env.example .env
   # Edit .env with your connection details
   

9. Configure external dependencies

   You'll need to set up port forwarding or connections to external services. For example:

   # Port forwarding from Kubernetes cluster
   kubectl port-forward svc/postgresql 5432:5432
   kubectl port-forward svc/mlflow 5000:5000
   kubectl port-forward svc/mongodb 27017:27017
   
   # Or connect to external services
   # Ensure services are accessible on localhost with appropriate ports
   

📦 How to Run

Running the Model Manager Application

Use the provided script to run the application locally:

# Make script executable (first time only)
chmod +x run_local.sh

# Run the application
./run_local.sh

The script will:

• Activate the virtual environment
• Load environment variables from .env
• Start the model-manager worker application

Running Tests and Coverage

Use the provided script to run tests with coverage:

# Make script executable (first time only)
chmod +x run_coverage.sh

# Run tests with coverage
./run_coverage.sh

The script will:

• Activate the virtual environment
• Run pytest with coverage reporting
• Generate HTML coverage report
• Open the coverage report in your browser

Manual Test Execution

You can also run tests manually:

# Activate virtual environment
source ./venv/bin/activate

# Run all tests
pytest

# Run with coverage
pytest --cov=model_manager --cov-report=html

# Run specific test categories
pytest tests/activities/
pytest tests/workflow/

Manual Application Execution

For manual execution without scripts:

# Activate virtual environment
source ./venv/bin/activate

# Load environment variables (if using .env file)
if [ -f .env ]; then
    export $(cat .env | grep -v '^#' | xargs)
fi

# Start the model-manager worker
python -m model_manager.worker.worker

🔍 Code Quality & Validation

Overview

Como Python não é uma linguagem compilada, utilizamos um conjunto robusto de ferramentas para validar a qualidade, segurança e correção do código antes da execução. Estas ferramentas detectam erros, problemas de estilo, vulnerabilidades de segurança e garantem a consistência do código.

Ferramentas de Validação

1. Ruff - Linting e Formatação ⚡

Ferramenta moderna e extremamente rápida (escrita em Rust) que substitui múltiplas ferramentas:

• Linting: Detecta erros de código, problemas de estilo (PEP 8), bugs comuns
• Formatação: Formata código automaticamente de forma consistente
• Velocidade: 10-100x mais rápido que Flake8/Black

2. mypy - Type Checking 🏷️

Verificador de tipos estáticos que analisa type hints:

• Detecta erros de tipo antes da execução
• Melhora a documentação do código
• Previne bugs relacionados a tipos incorretos

3. Bandit - Análise de Segurança 🔒

Scanner de vulnerabilidades de segurança:

• Detecta padrões inseguros de código
• Identifica hardcoded passwords, SQL injection, etc.
• Garante conformidade com práticas de segurança

4. pytest - Testes Automatizados 🧪

Framework de testes com cobertura de código:

• Executa testes unitários e de integração
• Mede cobertura de código
• Suporta testes assíncronos

Instalação das Ferramentas

# Instalar dependências de desenvolvimento
pip install -r requirements-dev.txt

Validação Completa

Opção 1: Script Automatizado (Recomendado)

# Executar todas as validações de uma vez
./validate.sh

O script validate.sh executa automaticamente:

1. ✅ Verificação de formatação (Ruff)
2. ✅ Linting de código (Ruff)
3. ✅ Type checking (mypy)
4. ✅ Análise de segurança (Bandit)
5. ✅ Testes unitários com cobertura (pytest)

Opção 2: Comandos Individuais

# 1. Verificar formatação
ruff format --check model_manager/ tests/

# 2. Verificar linting
ruff check model_manager/ tests/

# 3. Verificar tipos
mypy model_manager/

# 4. Análise de segurança
bandit -r model_manager/ -ll

# 5. Executar testes
pytest tests/ --cov=model_manager --cov-report=term-missing

Correção Automática

Algumas ferramentas podem corrigir problemas automaticamente:

# Formatar código automaticamente
ruff format model_manager/ tests/

# Corrigir problemas de linting automaticamente
ruff check --fix model_manager/ tests/

Configuração

Todas as ferramentas são configuradas no arquivo pyproject.toml:

• Ruff: Regras de linting, formatação, complexidade
• mypy: Configurações de type checking
• pytest: Opções de teste e cobertura
• Bandit: Regras de segurança

Integração com CI/CD

O workflow .github/workflows/quality-gate.yml executa automaticamente todas as validações em cada push/PR:

• ✅ Formatação e linting bloqueiam merge se falharem
• ⚠️ Type checking e segurança geram avisos mas não bloqueiam
• ✅ Testes devem passar com cobertura mínima de 70%

Boas Práticas

1. Antes de Commit: Execute ./validate.sh para garantir qualidade
2. Durante Desenvolvimento: Use ruff check --watch para feedback em tempo real
3. Type Hints: Adicione type hints em funções novas para melhor validação
4. Testes: Mantenha cobertura acima de 70%
5. Segurança: Revise e corrija todos os avisos do Bandit

🧪 Testing

Test Structure

tests/
├── activities/           # Activity implementation tests
├── workflow/            # Workflow orchestration tests
├── utils/               # Utility function tests
└── integration/         # End-to-end workflow tests

Test Execution

# Install test dependencies
pip install pytest pytest-cov pytest-asyncio

# Run tests with coverage
pytest --cov=model_manager --cov-report=html

# Run specific test modules
pytest tests/activities/test_gates.py
pytest tests/workflow/test_predictions_batch.py

📊 Monitoring and Metrics

The Model Manager system exposes comprehensive Prometheus metrics for operational visibility and performance monitoring:

Application Health Metrics

• app_up: Application health status (1=healthy, 0=unhealthy)
  • Labels: pod_id

Prediction Operation Metrics

• model_manager_predictions_written_count: Counter for successful prediction exports
  • Labels: pod_id, model_name, pipeline_name
• model_manager_prediction_confidence_monitor: Gauge for current prediction confidence levels
  • Labels: pod_id, model_name, pipeline_name
• model_manager_prediction_response_time_monitor: Histogram for prediction response times
  • Labels: pod_id, model_name, pipeline_name
  • Buckets: [0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]

Data Quality Metrics

• Filter pass/fail rates through notification system
• MLFlow API response validation metrics
• Data quality gate performance tracking

⚙️ Configuration

Environment Variables

Variable	Description	Default	Required
TEMPORAL_HOST	Temporal server address	localhost:7233	Yes
TEMPORAL_NAMESPACE	Temporal namespace	model-manager	No
POSTGRES_HOST	PostgreSQL hostname	localhost	Yes
POSTGRES_PORT	PostgreSQL port	5432	Yes
POSTGRES_USER	PostgreSQL username	sientia	Yes
POSTGRES_PASSWORD	PostgreSQL password	sientia	Yes
POSTGRES_DBNAME	PostgreSQL database	sientia	Yes
POSTGRES_MIN_CONNECTIONS	Minimum PostgreSQL connections	5	No
POSTGRES_MAX_CONNECTIONS	Maximum PostgreSQL connections	20	No
MLFLOW_HOST	MLFlow server hostname	http://localhost	Yes
MLFLOW_PORT	MLFlow server port	5080	Yes
MLFLOW_USERNAME	MLFlow username	aignosi	Yes
MLFLOW_PASSWORD	MLFlow password	aignosi	Yes
MONGODB_URL	MongoDB connection URI	localhost:27018	Yes
MONGODB_USERNAME	MongoDB username	root	Yes
MONGODB_PASSWORD	MongoDB password	wKZDbMNU1c	Yes
MONGODB_DATABASE_NAME	MongoDB database name	sientia	Yes
MONGODB_TTL_INDEX_HOURS	MongoDB TTL index hours	1	No
LOG_LEVEL	Application log level	INFO	No
PROJECT_NAME	Project name for metrics	model-manager	No
HTTP_METRICS_PORT	Prometheus metrics port	9090	No
HTTP_SDK_METRICS_PORT	Temporal SDK metrics port	9091	No
POD_ID	Kubernetes pod identifier	None	No

Workflow Configuration

MongoDB pipeline configuration:

Predictions Batch Workflow configuration sample

This is the configuration for the Predictions Batch Workflow, to be inserted into the MongoDB pipeline collection.

{
  "schedule_name": "laborious-orchestrated-pipeline",
  "model_id": "1",
  "workflow_type": "predictions_batch",
  "frequency": "30s",
  "max_retry_policy": 1,
  "query": "select * from sientia_data.laborious_data where model_id = 1 and \"timestamp\" > NOW() - INTERVAL '5 minutes' order by \"timestamp\" desc limit 30;",
  "write_tags": [
    {
      "server_id": "server1",
      "type": "prediction",
      "addr": "ns=2;i=5",
      "data_type": "double"
    },
    {
      "server_id": "server1",
      "type": "confidence",
      "addr": "ns=2;i=6",
      "data_type": "double"
    }
  ],
  "input_filters": {
    "EMPTY_DATA": {"POLICY": "STOP"},
    "SPECIFIC_VARIABLES_NULL_VALUES": {
      "POLICY": "CONTINUE",
      "config": {"variables": ["Counter"]}
    }
  },
  "mlflow_transform_filters": {
    "API_ERROR": {"POLICY": "REPEAT"},
    "NAN_VALUES": {"POLICY": "STOP"}
  },
  "mlflow_predict_filters": {
    "API_ERROR": {"POLICY": "CONTINUE"}
  },
  "path_priority": ["STOP", "CONTINUE", "REPEAT"],
  "active": true,
  "updated_at": {
    "$date": "2025-09-16T10:00:00.000Z"
  },
  "datetime_columns": ["timestamp", "created_at"],
  "predictions_storage_policy": "lts:1"
}

This is the configuration created by the Orchestrator in Temporal.

{
  "datetime_columns":["timestamp","created_at"],
  "frequency":"15m",
  "input_filters":{"EMPTY_DATA":{"config":{},"policy":"STOP"}},
  "max_retry_policy":1,
  "mlflow_predict_filters":{"API_ERROR":{"config":{},"policy":"CONTINUE"}},
  "mlflow_transform_filters":{
    "API_ERROR":{"config":{},"policy":"CONTINUE"},
    "EMPTY_DATA":{"config":{},"policy":"STOP"}
  },
  "model_config":{
    "is_compressed":true,
    "predict_flavor":"pyfunc",
    "retention_minutes":60,
    "retention_target":"artifact",
    "transform_function_keyword":"transform"
  },
  "model_id":"352",
  "model_name":"courier",
  "path_priority":["STOP","CONTINUE","REPEAT"],
  "predictions_storage_policy":"lts:1",
  "query":"select * from sientia_data.laborious_data where model_id = 352 order by \"timestamp\" desc limit 300;",
  "retention_time":3600,
  "schedule_name":"laborious-courier",
  "schema":"sientia_data",
  "table_name":"predictions",
  "updated_at":"2025-09-12 19:35:01.600000+0000",
  "workflow_type":"predictions_batch"
}

🔧 Development

Project Structure

model_manager/
├── activities/              # Temporal activity implementations
│   ├── activities.py       # Main activities orchestrator
│   ├── gates.py            # Data quality gates and filtering
│   └── mlflow.py           # MLFlow model operations
├── workflows/               # Temporal workflow definitions
│   ├── predictions_batch.py # Main batch prediction workflow
│   ├── minimal_retrain.py  # Model retraining workflow
│   └── sub_workflows/      # Sub-workflow implementations
│       ├── prediction_process.py # Core prediction workflow
│       └── format_and_export_prediction.py # Export workflow
├── worker/                  # Worker implementation
│   └── worker.py           # Main worker orchestrator
├── utils/                   # Utility functions
│   ├── connectors_config.py # Database configuration
│   ├── filters/            # Data quality filters
│   │   ├── conditional_filters.py # Conditional data filters
│   │   └── mlflow_filters.py # MLFlow response filters
│   └── repository/         # Data access layer
│       └── model_repository.py # MLFlow model operations
├── metrics.py               # Prometheus metrics definitions
└── __init__.py

Adding New Features

1. Follow Temporal patterns for new workflows and activities
2. Add comprehensive docstrings for all public methods
3. Include Prometheus metrics for monitoring
4. Add unit tests for new functionality
5. Update this README with new features and configuration

🐛 Troubleshooting

Common Issues

1. Temporal Connection Failures

   • Verify Temporal server is running and accessible
   • Check namespace configuration and permissions
   • Review server logs for connection issues

2. MLFlow Connection Issues

   • Verify MLFlow server is running and accessible
   • Check authentication credentials and permissions
   • Ensure model names and versions exist

3. Database Connection Issues

   • Verify PostgreSQL service is running
   • Check connection credentials and network access
   • Ensure proper connection pool configuration

4. Workflow Execution Failures

   • Review activity error logs and notifications
   • Check data quality filter configurations
   • Verify input data format and required fields

Debug Mode

Enable debug logging by setting the log level:

export LOG_LEVEL=DEBUG

⚡ Performance Tuning

Key Parameters

• Worker Concurrency: Adjust max_concurrent_workflow_tasks and max_concurrent_activities
• Connection Pools: Optimize database connection pool sizes
• Model Retention: Configure MLFlow model retention based on requirements
• Batch Sizes: Adjust data processing batch sizes for optimal throughput

Scaling Considerations

• Horizontal Scaling: Deploy multiple worker instances
• Task Queue Distribution: Use multiple task queues for different workflow types
• Database Performance: Optimize indexes and connection pooling
• MLFlow Performance: Configure appropriate model serving resources

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes with comprehensive testing
4. Update documentation and docstrings
5. Submit a pull request

Code Quality Standards

• Follow PEP 8 style guidelines
• Include comprehensive docstrings for all public methods
• Maintain test coverage above 80%
• Use type hints where appropriate
• Follow Temporal.io best practices

📄 License

This project is licensed under the terms specified in the LICENSE file.

🆘 Support

For support and questions:

• Check the troubleshooting section above
• Review the metrics and logs for error patterns
• Open an issue in the project repository
• Contact the development team

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Note: The Model Manager system is designed for production use in industrial ML environments. Ensure proper security configuration and network isolation for production deployments.