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/schedules/cleanup_schedule.py

@@ -116,7 +116,6 @@ async def create_cleanup_schedule(
     runtime = (os.getenv('RUNTIME') or 'single').strip()
     cleanup_task_queue = build_queue_name('CleanupFiles', runtime or 'single')
     schedule_id = build_cleanup_schedule_id(runtime)
-    created = False
     updated = False
 
     if await schedule_exists(client, schedule_id, logger, metadata):
@@ -147,15 +146,13 @@ async def create_cleanup_schedule(
             ),
         ),
     )
-    created = not updated
-
     if updated:
         logger.custom_info(
             f"Schedule '{schedule_id}' reconciled successfully. "
             f'Cleanup will run at: {CLEANUP_CRON} ({CLEANUP_TIMEZONE})',
             metadata,
         )
-    elif created:
+    else:
         logger.custom_info(
             f"Schedule '{schedule_id}' created successfully. "
             f'Cleanup will run at: {CLEANUP_CRON} ({CLEANUP_TIMEZONE})',