Update GITHUB_BRANCH in values.yaml and enhance error notification handling in MLFlow
- Changed GITHUB_BRANCH in values.yaml to reflect the new pipeline for alerts orchestration.
- Added NotificationLevel.ERROR to error notifications in MLFlow for retraining and production model updates.
- Updated tests to verify error notification levels for model management workflows.
Refactor model_repository and enhance test coverage for MLFlow functionalities
- Updated model_repository to ensure the 'temp' directory is created if it doesn't exist using `exist_ok=True`.
- Added new tests for retraining and updating production models, including error handling scenarios.
- Improved existing tests for model management workflows to ensure robustness and reliability.
Update GITHUB_BRANCH in values.yaml and refactor MLFlowRepository for model retraining
- Changed GITHUB_BRANCH in values.yaml to reflect the new pipeline for retraining.
- Refactored MLFlowRepository by introducing create_model_experiment and perform_model_retrain methods to streamline model retraining processes.
- Updated test cases to cover new functionalities and ensure proper error handling in model management workflows.
SIENTIAPDE-1163 Enhance OPC tests to improve error handling and notifications. Added tests for MongoDB configuration and refined connection error handling in OPC repository.
Refactor notification handling in Gates and OPC activities to use send_notification method with metadata integration, enhancing error reporting and traceability.
Refactor tests for Postgres activities and improve error handling
- Updated test_postgres.py to enhance the testing of load_custom_query method, including cases for None data and date conversion.
- Refactored repeat_last_prediction tests to use mocks for SQLAlchemy session execution.
- Added tests for export_data_to_postgres method, covering both success and error scenarios.
- Improved the initialization tests for Activities class to ensure proper instantiation of dependencies.
- Enhanced test coverage for OPC repository connection validation.
- Updated tests for prediction workflows to streamline input handling and improve clarity.
- Introduced tests for connectors configuration to validate environment variable handling for MLFlow, OPC, and Postgres.
- Added tests for logger utility to ensure default settings are correctly applied.
Refactor and enhance the laborious workflow and utilities
- Removed outdated test file `test_predictions_batch.py` from workflows.
- Added `input_sample.json` for standardized input configuration.
- Introduced `connectors_config.py` to manage database and service configurations.
- Implemented a logging utility in `logger.py` for consistent logging across the application.
- Created `policies.py` to define retry policies for workflows.
- Developed comprehensive tests for `MLFlowRepository` in `test_model_repository.py`.
- Added extensive tests for `OpcRepository` in `test_opc_repository.py`.
- Updated `test_predictions_batch.py` to reflect new workflow structure and testing methodology.
Implement new get_last_timestamp method in Gates class, refactor MLFlow activity methods to return only transformed data, and update PredictionsBatch and PredictionProcess workflows to utilize Activities module. Add detailed docstrings for new methods and enhance test coverage for get_last_timestamp functionality.
Refactor activity methods and update requirements.txt to enhance functionality and remove deprecated filters. Added detailed docstrings for clarity and improved error handling in data processing workflows.
