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.
Refactor data handling in various modules to ensure DataFrame consistency
- Replaced direct DataFrame instantiation with `ensure_dataframe` utility in Gates, MLFlow, OPC, and ModelMetrics classes to standardize data handling.
- Updated return types in several asynchronous methods to return DataFrames instead of dictionaries for improved usability.
- Adjusted data export processes in workflows to convert DataFrames to dictionaries with `to_dict(orient='records')` for compatibility with downstream systems.
Enhance MLFlowRepository and Activities classes with new methods and metrics
- Added `check_artifact_exists` method to MLFlowRepository for verifying artifact presence in the MLflow Model Registry.
- Implemented `get_prediction_data` method in MLFlowRepository to retrieve prediction data from models.
- Updated Activities class to integrate ModelMetrics for improved metrics handling.
- Enhanced tests for artifact existence checks and prediction data retrieval, ensuring robust coverage for new functionalities.
- Updated various workflows to include `transform_table_name` in input data for better data handling.
Enhance metrics handling in Gates and MLFlow classes
- Added checks for `None` response times before emitting OPC writing metrics in the Gates class to prevent unnecessary metric emissions.
- Updated the MLFlow class to conditionally sort and drop duplicates based on the presence of the 'created_at' column, ensuring robustness in data processing.
- Adjusted corresponding tests to validate the new behavior in both classes.
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 .gitignore and refactor metrics.py for improved logging and consistency
- Added coverage.xml to .gitignore to prevent tracking of coverage reports.
- Refactored metric labels in metrics.py for consistency in string formatting and improved readability.
- Enhanced logging messages in various activities to ensure uniformity in message formatting.
Enhance Gates and MLFlowRepository with new functionalities and improvements
- Added a new method `clean_tmp_files` in the Gates class to remove temporary files associated with model retraining.
- Updated MLFlowRepository methods to improve experiment handling, including dynamic parameter logging and model retrieval.
- Refactored model loading methods to streamline the process and enhance error handling.
- Improved logging for model operations and added support for model parameter retrieval.
- Adjusted minimal_retrain workflow to extend timeouts for activities and ensure proper model configuration handling.
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.
Enhance MLFlow and MLFlowRepository with improved data handling and logging
- Refactored MLFlow class to sort data by 'created_at' and drop duplicates for better input preparation.
- Updated MLFlowRepository methods to include detailed logging for artifact downloads and model predictions.
- Introduced LzmaPayloadCodec for efficient payload compression in the worker, optimizing data handling for large payloads.
- Enhanced timestamp handling in treated data to ensure compatibility with model expectations.
Update model retraining and logging enhancements
- Changed the GITHUB_BRANCH value in values.yaml to 'main' for consistency.
- Refactored MLFlow class to improve timestamp handling and error messaging during model retraining.
- Enhanced MLFlowRepository methods to include metadata logging and improved model version retrieval.
- Updated minimal_retrain workflow to support extended timeout for activities and include model configuration in input data.
Update model retraining and reporting functionality
- Changed the GITHUB_BRANCH value in values.yaml to reflect the latest adjustments for retraining the courier.
- Enhanced the Gates class with a new method `format_retrain_report` to format retraining report data according to storage policies.
- Refactored the MLFlow class to improve error handling during model retraining and return structured output.
- Updated the model_repository to utilize the latest MLFlow API for retrieving model versions and improved logging.
- Modified the minimal_retrain workflow to conditionally update the production model based on retraining success.
SIENTIAPDE-1222
Enhance MLFlow and MLFlowRepository with model configuration support
- Introduced `model_config` parameter in MLFlow methods to streamline model handling and configuration management.
- Updated `retrain_model`, `transform`, and `predict` methods to accept `model_config` and `metadata` for improved flexibility and logging.
- Added `detect_and_parse_datetime_index` method to handle datetime index parsing with enhanced error handling and logging.
- Refactored model experiment creation to include transformation and prediction flavors, along with compression options.
- Improved documentation and type hints across methods for better clarity and usability.
Enhance MLFlow data handling by adding timestamp column and improving debug logging
- Added a 'timestamp' column to the input data, converting the index to a datetime format for better tracking of predictions.
- Improved debug logging to provide clearer context by including the input data preview in the log output.
Update MLFlow class to pass logger instance directly to MLFlowRepository
- Modified the initialization of MLFlowRepository in the MLFlow class to pass the logger instance directly, improving logging capabilities and consistency across the application.
SIENTIAPDE-1222 Update MLFlow methods to include metadata parameter
- Modified the transform and predict method calls in the MLFlow class to include a new 'metadata' parameter, enhancing the functionality and data handling capabilities of the model monitoring repository.
Refactor datetime index handling in MLFlow and MLFlowRepository
- Moved the detect_and_parse_datetime_index method from MLFlow to MLFlowRepository for better organization and reusability.
- Updated the method to include enhanced logging and error handling for invalid datetime formats.
- Adjusted the transform method in MLFlowRepository to utilize the new datetime index parsing logic.
- Added unit tests for both valid and invalid datetime index cases to ensure robustness.
Enhance MLFlow debug logging to limit output size
- Updated debug logging statements in the MLFlow activity to include a maximum of 5 items and a depth of 5 for the sample dictionary, improving readability and reducing log clutter for raw and transformed response data.
Refactor MLFlow debug logging for improved readability
- Reformatted debug logging statements in the MLFlow activity to enhance clarity and consistency.
- Ensured that the output of raw and transformed response data is presented in a more readable format, maintaining the use of create_sample_dict for better visualization.
Update image tag in values.yaml and enhance debug logging in Gates and MLFlow activities
- Updated the image tag in values.yaml from '0.0.1' to '0.0.2'.
- Improved debug logging in the Gates activity to format input data and filters for better readability.
- Enhanced MLFlow activity logging to include formatted output for raw and transformed response data, ensuring consistent logging format.
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-1222
Enhance MLFlow logging with sample dictionary for response data
- Introduced a new method to create a sample dictionary for debugging, allowing for better visualization of nested data structures in logs.
- Updated debug logging to utilize the new sampling method for raw and transformed response data, improving clarity and reducing output size.
- Adjusted logging for processed input data to display only the first few rows, enhancing readability.
Refactor MLFlow logging to enhance data output clarity
- Updated debug logging to use data.to_string() for processed input data, improving readability.
- Modified raw response data logging to format the output as a string, ensuring consistent logging format.
Update values.yaml and MLFlow logging for courier integration
- Changed the image repository to 'sientia-module-courier' and updated the image tag to '0.0.1'.
- Modified environment variables for GITHUB_BRANCH and MLFLOW_PASSWORD to reflect new configurations.
- Enhanced MLFlow logging to include additional debug statements for raw response data and added a check for empty DataFrames.
Refactor MLFlow logging to improve data output clarity
- Updated the debug logging to directly capture the output of data.to_csv, enhancing traceability of processed input data.
- Removed redundant debug statements for raw response data to streamline logging and focus on essential information.
SIENTIAPDE-1214: Enhance MLFlow and tests with datetime index handling and logging improvements
- Added a new method in MLFlow to detect and parse datetime indices in DataFrames, ensuring proper format and raising errors for invalid types.
- Updated prediction workflows to utilize the new datetime index handling, improving data integrity during transformations.
- Enhanced logging in model_repository to include detailed data outputs for better traceability.
- Adjusted timeout settings in prediction workflows for improved execution time management.
- Updated tests.ipynb to include additional checks for index types and outputs for better validation of functionality.
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.
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.
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.
Refactor logging in Gates and MLFlow activities to use info level for key operations
- Updated logging statements in the Gates class to replace debug logs with info logs for input and output gate operations, enhancing visibility.
- Modified MLFlow class to use info logs for data transformation and prediction processes, improving clarity in the logging output.
- Adjusted OPC class to return the count of successfully written tags, providing better insight into data writing operations.
Enhance logging in MLFlow activity to include formatted JSON response data
- Added JSON import for better formatting of response data in debug logs.
- Updated logging statements to output transformed and prediction response data as pretty-printed JSON, improving readability and debugging context.
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.
Enhance BaseActivity initialization across multiple activities to include error counter
- Updated the initialization of the BaseActivity in Gates, MLFlow, and OPC classes to set the error counter to True, improving error tracking and handling capabilities.
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 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.
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.
Update notification handler references to use CoreNotificationHandler and add MongoDB configuration to connectors. Update GITHUB_BRANCH in values.yaml for optimization tasks.
Refactor logging in MLFlow and Gates activities; remove print statement in FormatAndExportPrediction; update data handling in PredictionProcess; delete unused redis-feeder script.
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.
