This commit refactors the MLFlow activities to focus on model training rather than prediction operations. It removes prediction-related activities and metrics, and updates the MLFlow activity descriptions to reflect the change in focus. The README is also updated to reflect these changes.
This commit refactors the training workflow and associated activities to raise exceptions on failure instead of returning success/failure dictionaries. This allows the Temporal workflow to handle errors more effectively and ensures that the workflow stops when a critical error occurs.
Key changes:
- The train_model workflow is introduced to orchestrate the entire training process, including parameter validation, data download, model training, and model saving.
- The validate_train_params activity is added to validate and convert training parameters.
- The train_model and save_model activities are updated to raise exceptions on failure.
- The ExperimentStatus enum is updated to include a new status for orchestrator validation errors.
- The tests are updated to reflect the new exception-based error handling.
- The activities now return the TrainModelResult directly instead of a dictionary.
