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.
Refactor Activities and API Integration for PI Web API
- Reintroduced the API import in the Activities class for proper integration.
- Cleaned up whitespace and formatting in the API class and related tests for improved readability.
- Updated test cases to ensure consistent formatting in error messages and configuration structures for PI Web API.
- Enhanced connectors_config.py with additional whitespace for better organization.
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.
SIENTIAPDE-1325 Add OPC server name configuration and update repository handling
- Introduced a new environment variable `OPC_SERVER_NAME` in values.yaml with a default value.
- Updated the OPC class to include `server_name` when initializing OpcRepository.
- Enhanced the configuration builder to retrieve `OPC_SERVER_NAME` from the environment.
- Adjusted OpcRepository to store and utilize the `server_name` for metric emissions.
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 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.
Refactor OPC and model repository for improved functionality and clarity
- Updated OPC server logging to handle missing prediction and confidence tags gracefully.
- Corrected documentation for OPC reconnection interval from milliseconds to seconds.
- Enhanced MLFlowRepository with new methods for model retrieval, caching, and transformation, improving model management and retraining workflows.
Refactor connectors_config.py and conditional_filters.py for improved configuration management and data filtering
- Updated PostgreSQL and MLFlow configuration functions to enhance default values and environment variable handling.
- Simplified OPC server configuration logic and improved MongoDB connection string construction.
- Refactored conditional filters to streamline null value checks and empty data validation, removing unnecessary comments and examples for clarity.
- Removed extensive module docstrings to enhance code readability.
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 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.
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 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.
