Enhance environment configuration and update dependencies
- Added new environment variables for PluginStore and MLflow configuration in `.env.example`, including `RUNTIME`, `STORE_BASE_URL`, `STORE_OWNER`, `STORE_REPO`, `STORE_BRANCH`, `STORE_USERNAME`, `STORE_PASSWORD`, `STORE_CACHE_TTL_SECONDS`, `PYPI_SERVER`, `PYPI_USERNAME`, and `PYPI_PASSWORD`.
- Updated `git-requirements-mapping.txt` to reflect changes in repository names.
- Modified `requirements-light.txt` and `requirements.txt` to upgrade `sientia-dataops-library` to version 1.12.0 and `sientia-mlops-library` to version 0.8.1.
- Updated `values.yaml` to include new environment variables for worker runtime and PluginStore configuration.
- Refactored E2E tests to utilize new MLflow repository stubs and PluginStore mocks for improved testing accuracy.
Enhance README and Implement Drift Detection and Metrics Workflows
- Added new sections in README for Drift Workflow and Simple Metrics Workflow, detailing their execution flows and functionalities.
- Introduced `drift.py` for data drift detection, comparing current data against reference datasets.
- Added `simple_metrics.py` for calculating regression metrics (RMSE, MSE, MAE, R²).
- Updated `values.yaml` to include configuration for MinIO retention hours and offload threshold.
- Refactored `minio_dataframe_payload.py` to use the new offload threshold environment variable.
- Adjusted tests to reflect changes in environment variable handling for MinIO offload threshold.
Implement MinIO Offload and Retention Features
- Added configuration options for MinIO retention hours and offload threshold in README.
- Introduced MinIO payload offloading for large DataFrame-derived payloads, storing them as parquet files.
- Updated activities to utilize MinIO for data loading and cleanup, including new methods for offloading and retention management.
- Refactored existing activities to integrate MinIO functionality, ensuring compatibility with previous workflows.
- Removed the legacy MinioRepository class, consolidating MinIO operations under a new manager structure.
- Updated requirements to use the latest version of the sientia-dataops-library.
Enhance README and Codebase with PI Web API Integration
- Updated README.md to include details about PI Web API integration, including configuration and export capabilities.
- Modified Activities class to incorporate PI Web API export operations and error handling.
- Added new API class for handling PI Web API interactions, including writing prediction and confidence data.
- Updated prediction workflows to support PI Web API output configuration.
- Enhanced worker and sub-workflows to include PI Web API in task queues and export processes.
- Improved documentation and error handling for PI Web API connections and configurations.
Enhance data handling and export processes in Laborious workflows
- Updated `gates.py` to improve data quality validation, filtering, and formatting operations, including enhanced metrics recording.
- Refined `mlflow.py` to better manage model transformations and reference data retrieval from MLflow Model Registry.
- Enhanced `format_and_export_prediction.py` to support separate export of transformed data, improving flexibility in data handling.
- Added comprehensive test coverage for new functionalities, including transformed data formatting and retrain report generation.
- Improved documentation in `README.md` to reflect changes in activities and workflows, ensuring clarity on data processing and export paths.
Refactor monitoring and metrics integration across various components
- Removed coverage options from `pyproject.toml`.
- Updated prediction metrics in `README.md` to replace `pipeline_name` with `workflow_name`.
- Upgraded `sientia-dataops-library` dependency version in `requirements-light.txt` and `requirements.txt`.
- Enhanced metrics handling in `laborious` activities, including `Activities`, `Gates`, `MLFlow`, and `OPC`, to utilize a new `MetricsController`.
- Refactored metric emission methods to improve clarity and consistency across the codebase.
- Updated tests to reflect changes in metrics handling and ensure proper functionality.
Enhance README with code quality and validation guidelines
- Added a new section on code quality and validation tools, detailing the use of Ruff, mypy, Bandit, and pytest.
- Included installation instructions for development dependencies and options for running validation scripts.
- Provided best practices for maintaining code quality and integrating CI/CD workflows.
Enhance README and repository utilities for clarity and functionality
- Updated README.md to improve descriptions and structure, adding detailed sections for features, workflows, and architecture.
- Enhanced MinioRepository with comprehensive docstrings for methods and class attributes, improving usability and documentation.
- Refined MLFlowRepository with clearer method descriptions and improved logging for better observability and maintainability.
Update README.md to reflect new features and configuration changes
- Added details about two dedicated task queues: `predictions_batch-queue` and `minimal_retrain-queue`.
- Enhanced descriptions of activities and workflows, including multiple inheritance patterns and configurable MLFlow model serving.
- Updated monitoring metrics section to include new labels and metrics for prediction and OPC export operations.
- Revised configuration section with updated default values and added new environment variables for Kubernetes pod identification.
- Improved clarity in the Predictions Batch Workflow configuration example, including structured input filters and updated retention policies.
Update README.md to enhance installation instructions and refactor MLFlow filters
- Added steps for installing GitHub CLI and authenticating with GitHub.
- Updated the `api_error_filter` and `nan_values_filter` functions to improve parameter handling and streamline logic.
Update README.md to enhance architecture documentation
- Removed detailed system overview diagram to streamline content.
- Added architecture diagrams for key workflows: PredictionsBatch, PredictionProcess, FormatAndExportPrediction, and MinimalRetrain.
- Improved clarity and structure of the architecture principles section.
Update README.md to reflect changes in prediction workflow configuration
- Revised input parameters for the prediction process, including updates to schedule name, model ID, and workflow name.
- Enhanced structure of input filters and MLflow filter policies for improved clarity and functionality.
- Introduced new fields for model retention and output configuration, while maintaining backward compatibility with existing parameters.
Update README.md to reflect new prediction workflow configuration
- Revised input parameters for the prediction process, including changes to schedule name, model ID, and workflow type.
- Introduced new fields for workflow execution frequency, maximum retry policy, and query for data retrieval.
- Updated input and MLflow filter structures to enhance clarity and functionality.
- Added support for datetime columns and updated retention time for models.
Remove Docker configuration files and refactor project structure
- Deleted docker-compose.yml and Dockerfile as part of the project restructuring.
- Updated README.md to reflect changes in project setup and configuration.
- Introduced a new __init__.py file in the laborious package to provide an overview of the system.
- Enhanced documentation across various modules, including metrics, activities, and workflows, to improve clarity and usability.
- Added comprehensive docstrings and comments to key classes and methods for better maintainability.
