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.
4.1 KiB
4.1 KiB