Update dependencies and refactor input filter handling for consistency
- Updated sientia-dataops-library dependency version from 1.10.3 to 1.10.4 in requirements.txt.
- Refactored input filter handling in the Gates class to read policy and config keys in a case-insensitive manner.
- Updated test cases to ensure consistency in filter key naming conventions across various scenarios.
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.
Update sientia-dataops-library dependency version from 1.8.1 to 1.8.2 in requirements.txt and modify API class to accept a list of response data for PI Web API integration.
Update requirements.txt and values.yaml for dependency and image tag adjustments
- Replaced local path with the correct GitHub URL for sientia-dataops-library in requirements.txt.
- Downgraded the image tag from "1.1.2" to "1.0.1" in values.yaml for consistency with deployment requirements.
SIENTIAPDE-1478
Implement PI Web API response processing and metrics tracking
- Added a new method in the API class to process responses from the PI Web API, validating tag writes and emitting metrics for success and errors.
- Enhanced error handling for missing WebIds and tag names in responses, with appropriate logging and notifications.
- Updated tests to cover various scenarios for processing PI Web API responses, ensuring robust functionality and metrics emission.
- Refactored existing methods to integrate the new response processing logic, improving overall code clarity and maintainability.
Enhance Activities and Prediction Workflows with PI Web API Integration
- Updated the Activities class to include API integration, allowing for configuration of PI Web API parameters.
- Modified prediction workflows to support output configuration for PI Web API, enabling data writing to the API.
- Refactored connectors_config.py by removing unused PostgreSQL and MongoDB configuration functions.
- Added tests to validate the new PI Web API functionality in activities and workflows, ensuring robust integration and functionality.
Update sientia-mlops-library dependency to version 0.40.6 and refactor get_prediction_data method calls for improved readability in model_repository.py and test_model_repository.py.
Update sientia-mlops-library dependency to version 0.40.5 and fix SQL interval formatting in Drift and SimpleMetrics workflows for improved query accuracy.
Update sientia-dataops-library dependency version to 1.6.1 and refactor return types in Gates class methods for improved type clarity. Changed return types from dict[str, Any] to dict for better compatibility with downstream systems.
Update dependencies and refactor data handling in various modules
- Updated sientia-dataops-library dependency version from 1.5.3 to 1.5.4 in requirements files.
- Updated sientia-mlops-library dependency version from 0.39.0 to 0.40.2 in requirements files.
- Refactored return types in Gates, MLFlow, and ModelMetrics classes to return dictionaries instead of DataFrames for improved compatibility with downstream systems.
- Removed the temporal_codec module as it is no longer needed for DataFrame serialization.
- Adjusted data handling in the Drift workflow to ensure proper data structure is maintained.
Update requirements and enhance metrics and data handling
- Updated the sientia-dataops-library dependency version in requirements.txt to 1.5.3.
- Added new metrics for model analysis, including lag, count, and error count in metrics.py.
- Implemented a new method for formatting transformed data in gates.py.
- Enhanced MLFlowRepository with methods to load artifact dataframes and calculate model metrics, including drift and performance metrics.
- Updated the prediction process to handle transformed data and ensure proper execution of related activities in format_and_export_prediction.py and prediction_process.py.
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.
Update requirements and simplify CI workflow for quality checks
- Added new dependencies: kaleido, hyperopt, and shap to requirements.txt.
- Refactored the quality gate workflow to use a template for improved maintainability and reduced complexity.
Update dependencies and enhance OpcRepository functionality
- Updated sientia-dataops-library version to 1.4.7 in requirements files.
- Modified GITHUB_BRANCH in values.yaml for improved pipeline management.
- Refactored OpcRepository class to inherit from BaseActivity, adding enhanced logging and error handling during disconnection.
- Implemented a disconnection fallback mechanism to ensure graceful handling of OPC server disconnections.
Enhance MinIO integration and update environment configurations
- Added MinIO configuration parameters to .env.example and values.yaml for improved storage management.
- Updated requirements.txt to include necessary libraries for MinIO support.
- Refactored Activities class to utilize Storage for MinIO interactions.
- Enhanced MLFlow class to integrate MinIO for data retrieval during model retraining.
- Introduced build_minio_config function to streamline MinIO configuration setup.
- Updated minimal_retrain workflow to support data storage in MinIO.
Update requirements and enhance logging in Gates and MLFlow activities
- Updated the sientia-dataops-library and sientia-mlops-library dependencies in requirements.txt to the latest versions.
- Improved debug logging in the Gates activity to display a sample of input data and filters, enhancing clarity and reducing output size.
- Refactored MLFlow activity logging to utilize the create_sample_dict function for better visualization of nested data structures in logs.
SIENTIAPDE-1214: Refactor MLFlow and model repository methods to use model_config dictionary
- Updated MLFlow class methods to accept model_config instead of model_retention for improved flexibility.
- Modified model_repository methods to handle model_config, extracting necessary parameters for transformation and prediction.
- Adjusted predictions_batch and prediction_process workflows to utilize model_config for better configuration management.
- Commented out the previous sientia-mlops-library dependency in requirements.txt for clarity.
Update requirements and modify MLFlow activity
- Bump sientia-mlops-library version in requirements.txt from 0.38.5 to 0.38.8.
- Comment out the reset_index call in the MLFlow activity to prevent unintended data manipulation.
Update dependencies and enhance datetime handling
- Bump sientia-dataops-library version in requirements.txt to 1.4.4.
- Update image tag in values.yaml to 0.4.4.
- Add 'datetime_columns' to metadata in MinimalRetrain and PredictionsBatch workflows for improved data handling.
Update requirements and refactor Gates activity for improved datetime handling
- Update sientia-dataops-library reference in requirements.txt to version 1.4.3.
- Refactor datetime handling in Gates activity to use a consistent format with timezone support, replacing direct datetime calls with a centralized now() function and DATETIME_FORMAT_WITH_TZ constant.
Update requirements and values for SIENTIAPDE-1193
- Bump sientia-dataops-library version from 1.4.1 to 1.4.2 in requirements.txt.
- Change GITHUB_BRANCH in values.yaml to reflect the new task SIENTIAPDE-1193 regarding datetime writing in temporal.
Update dependencies in requirements.txt and increment image tag in values.yaml
- Updated sientia-dataops-library dependency version from 1.4.0 to 1.4.1 in requirements.txt.
- Incremented image tag in values.yaml from 0.4.1 to 0.4.2.
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.
Update dependencies and configuration for improved functionality
- Updated sientia-dataops-library version from 1.3.7 to 1.3.8 in requirements.txt.
- Changed GITHUB_BRANCH in values.yaml to reflect new testing focus: SIENTIAPDE-1169.
- Modified notification level in gates.py from WARNING to ERROR for better error handling.
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.
Update dependencies in requirements.txt and increment image tag in values.yaml
- Updated sientia-dataops-library version from 1.3.4 to 1.3.5 in requirements.txt.
- Incremented image tag in values.yaml from 0.2.5 to 0.2.7.
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.
