feat: update training workflow and repository management

- Replaced synchronous MinIO repository calls with asynchronous counterparts in the Training class for improved performance.
- Enhanced logging throughout the training process to provide better insights into model metadata loading, parameter validation, and training execution.
- Updated the train_test_split function to enforce DataFrame input type, ensuring consistency in data handling.
- Removed the deprecated model_repository.py file to streamline the codebase.
- Adjusted cleanup schedule logic to improve error handling and logging during schedule reconciliation.
- Updated tests to reflect changes in the training workflow and repository interactions.

model_manager/workflows/train_model.py

@@ -93,6 +93,8 @@ class TrainModel:
         Raises:
             ValueError: If experiment_run_id is missing or invalid
         """
+        workflow.logger.info(f'Starting train_model workflow for {input_data}')
+
         experiment_run_id = self._validate_experiment_run_id(input_data)
         input_data = {**input_data, 'experiment_run_id': experiment_run_id}