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.

model_manager/activities/experiment_tracking.py

@@ -246,7 +246,7 @@ class ExperimentTracking(Postgres):
             ValueError: If required parameters are missing for the update type
             RuntimeError: If update operation fails
         """
-        metadata = input_data.get('metadata', {})
+        metadata = input_data.get('metadata')
         experiment_run_id = input_data['experiment_run_id']
         update_type = input_data['update_type']
         status = input_data.get('status')
@@ -272,8 +272,8 @@ class ExperimentTracking(Postgres):
             error_msg = f'Error updating experiment run - ID: {experiment_run_id}, Status: {status}, Error: {str(e)}'
             trace = traceback.format_exc()
 
-            self.send_notification(
-                metadata=metadata,
+            await self.send_notification_async(
+                metadata=metadata or {},
                 notification_id='UPDATE_EXPERIMENT_RUN_ERROR',
                 message=error_msg,
                 block='update_experiment_run',