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.

tests/workflows/test_train_model.py

@@ -290,9 +290,11 @@ async def test_run_training_failure_skips_cleanup_activity(
 
 
 @pytest.mark.asyncio
-async def test_run_missing_experiment_run_id():
+@patch('model_manager.workflows.train_model.workflow')
+async def test_run_missing_experiment_run_id(mock_wf):
     from model_manager.workflows.train_model import TrainModel
 
+    mock_wf.logger = Mock()
     with pytest.raises(ValueError, match='experiment_run_id is required'):
         await TrainModel().run({})