feat: enhance training workflow with model metadata loading and refactor data handling

- Introduced a new activity to load model metadata from the model store.
- Refactored training logic to utilize new model metadata and improved parameter handling.
- Updated the `TrainModelParams` class to include additional fields for model configuration.
- Replaced deprecated utility functions with a custom train-test split implementation.
- Removed unused utility functions and cleaned up the data manager repository.
- Adjusted experiment tracking to include model-specific metadata in notifications.

tests/activities/test_training.py

@@ -55,7 +55,7 @@ def mock_train_params():
     params = MagicMock()
     params.experiment_run_id = 1
     params.target_variable = 'target'
-    params.experiment_name = 'test_experiment'
+    params.model_name = 'Linear Regression'
     params.bucket_name = 'test-bucket'
     params.file_name = 'test-file.csv'
     params.validate_business_rules = MagicMock()