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/worker/worker.py

@@ -161,14 +161,15 @@ async def main():
 
     logger.custom_info(f'SDK metrics server initialized on port {SDK_METRICS_PORT}', metadata)
 
+    namespace = os.getenv('TEMPORAL_NAMESPACE', 'model-manager')
     temporal_client = await client.Client.connect(
         target_host=host,
-        namespace=os.getenv('TEMPORAL_NAMESPACE', 'model-manager'),
+        namespace=namespace,
         runtime=new_runtime,
         tls=use_tls,
     )
 
-    logger.custom_info(f'Temporal client initialized at {host}', metadata)
+    logger.custom_info(f'Temporal client initialized at {host}/{namespace}', metadata)
 
     # Create cleanup schedule (idempotent - only creates if doesn't exist)
     try: