This commit introduces artifact generation and MLflow logging capabilities to the model training process. It includes the following changes:
- Added methods to generate reports, save data files, and log model parameters, metrics, models, and artifacts to MLflow.
- Implemented error handling for various scenarios, such as missing files, invalid data, and MLflow connection errors.
- Created a new 'header.html' file for report styling and navigation.
- Modified the 'model_repository.py' file to include the new artifact generation and MLflow logging methods.
- Added comprehensive unit tests to ensure the functionality and robustness of the new features.
