Enhance logging in OpcRepository to include metadata for all log messages
- Introduced a metadata dictionary to the OpcRepository class for improved context in logging.
- Updated logging statements to utilize custom_info, custom_warning, and custom_error methods, ensuring all key operations and error messages include relevant metadata.
- Improved visibility of connection status, disconnection attempts, and error handling, facilitating better monitoring and debugging.
Refactor logging in OpcRepository to include full metadata in error and info messages
- Updated logging statements in the OpcRepository class to pass the complete metadata object instead of just the schedule name, enhancing the context available for debugging and monitoring.
Update dependencies and refactor logging imports for observability
- Updated the sientia-dataops-library dependency version to 1.4.0 in requirements.txt.
- Changed image tag in values.yaml from 0.3.2 to 0.4.1.
- Refactored logging imports across multiple files to use the new observability module instead of the temporal.utils.logger.
- Updated retry policy imports in workflow files to reflect the new module structure.
Refactor OPC data writing and repository initialization
- Simplified success tracking logic in the OPC class for writing prediction and confidence data.
- Removed unused pod_id attribute from OpcRepository initialization.
- Updated test cases to include pod_id for improved metrics tracking during data writing operations.
Refactor opc_repository.py to import metrics module
- Added import statement for the metrics module to enhance functionality in the OPC repository.
- This change supports the integration of metrics tracking for OPC operations.
Implement OPC writing metrics and enhance OPC class initialization
- Added metrics for counting predictions written to the OPC server and monitoring their response times.
- Enhanced the OPC class initialization to include the pod ID for better tracking.
- Updated the write method to increment the prediction count and observe response times.
SIENTIAPDE-1174 Update replica count and enhance logging in MLFlow
- Changed replicaCount in values.yaml from 3 to 1 for reduced resource usage.
- Added debug logging for prediction response data in MLFlow to improve traceability.
Update dependencies, modify replica count, and implement metrics tracking
- Updated sientia-dataops-library version from 1.3.5 to 1.3.7 in requirements.txt.
- Changed replicaCount in values.yaml from 5 to 3 and incremented image tag from 0.2.7 to 0.3.1.
- Added Prometheus metrics tracking in gates.py and worker.py, including a new write_metrics method.
- Configured Prometheus service and ServiceMonitor in values.yaml for metrics collection.
Refactor data merging in MLFlowRepository to simplify merge operation in fit method. Removed unnecessary parameters for improved readability and maintainability.
Enhance data merging in MLFlowRepository by specifying the 'on' parameter for improved join accuracy. This change ensures that the merge operation correctly aligns data based on the 'id' field.
Enhance data merging in MLFlowRepository by specifying merge parameters
- Updated the merge operation in the fit method to include 'how' and 'validate' parameters for improved data integrity and flexibility during model training.
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.
Update dependencies and enhance ML model retraining functionality
- Updated sientia-dataops-library version in requirements.txt from 1.3.3 to 1.3.4.
- Incremented image tag in values.yaml from 0.2.4 to 0.2.5 and added a new environment variable MONGODB_TTL_INDEX_HOURS.
- Introduced new methods in MLFlowRepository for model retraining and production model updates, including error handling and logging.
- Added retrain_model and update_production_model activities in mlflow.py to support model management workflows.
- Modified MongoDB connection settings in connectors_config.py for improved security and configuration flexibility.
Refactor OPC connection handling to improve error notifications and update GITHUB_BRANCH in values.yaml for MongoDB integration. Change requirements.txt to point to local dataops library path.
Update notification handler references to use CoreNotificationHandler and add MongoDB configuration to connectors. Update GITHUB_BRANCH in values.yaml for optimization tasks.
Refactor notification handling in Gates and OPC activities to use send_notification method with metadata integration, enhancing error reporting and traceability.
Refactor Gates and OpcRepository for improved logging and error handling. Removed debug statement in Gates and enhanced disconnect method in OpcRepository to log disconnection status and handle exceptions.
Refactor OPC activity and OpcRepository to include metadata parameter in write_data methods, improving error handling and logging capabilities for better traceability.
SIENTIAPDE-1110 Refactor OPC activity to use server IDs instead of names, update values.yaml for OPC_ID, and enhance OpcRepository initialization for improved clarity and consistency.
Refactor logging in MLFlow and Gates activities; remove print statement in FormatAndExportPrediction; update data handling in PredictionProcess; delete unused redis-feeder script.
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.
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.
