feat: enhance training model functionality and reporting

- Added `evidently` to requirements for improved model evaluation.
- Introduced `TrainModelResult` class with a `to_dict` method for better result handling.
- Updated `train_model` method to return a comprehensive training result, including run details.
- Enhanced `cleanup_run_directory` method in `DataManagerRepository` for improved resource management.
- Adjusted type hints in `TrainModel` for clarity and consistency.

model_manager/activities/training.py

@@ -23,6 +23,9 @@ with workflow.unsafe.imports_passed_through():
 
     from model_manager.utils.models.train_model_params import TrainModelParams
     from model_manager.utils.repository.data_manager_repository import DataManagerRepository
+    from model_manager.utils.models.train_model_result import TrainModelResult
+
+    import mlflow
 
 
 class Training(SientiaMonitoring):
@@ -146,7 +149,7 @@ class Training(SientiaMonitoring):
             raise
 
     @activity.defn(name='train_model')
-    async def train_model(self, input_data: dict[str, Any]) -> dict[str, str | None]:
+    async def train_model(self, input_data: dict[str, Any]) -> dict[str, Any]:
         """
         Train a machine learning model.
 
@@ -246,22 +249,24 @@ class Training(SientiaMonitoring):
                 tags=None,
                 metadata=metadata,
             ) as run_info:
+                train_result.run_name = run_info.run_name
+                train_result.run_id = run_info.run_id
+
+                train_result = self.data_manager_repository.generate_report(
+                    train_result,
+                    metadata=metadata,
+                )
+
+                if train_result.report_path is None or train_result.train_data_path is None or train_result.test_data_path is None:
+                    raise ValueError('Report path, train data path, or test data path is not set')
+
                 wrapper.store_model(name=train_params.model_name)
 
-            train_result.run_name = run_info.run_name
-            train_result.run_id = run_info.run_id
+                mlflow.log_artifact(train_result.report_path)
+                mlflow.log_artifact(train_result.train_data_path)
+                mlflow.log_artifact(train_result.test_data_path)
 
-            train_result = self.data_manager_repository.generate_report(
-                train_result,
-                metadata=metadata,
-            )
-
-            model_saved = True
-
-            return {
-                'run_name': run_info.run_name,
-                'run_id': run_info.run_id,
-            }
+            return train_result.to_dict()
         except Exception as e:  # noqa: BLE001
 
             error_msg = f'Error training model - error: {str(e)}'
@@ -296,7 +301,7 @@ class Training(SientiaMonitoring):
         run_dir = input_data.get('run_dir', '')
 
         try:
-            self.model_repository.cleanup_run_directory(run_dir)
+            self.data_manager_repository.cleanup_run_directory(run_dir, metadata)
         except Exception as e:  # noqa: BLE001
             error_msg = f'Error cleaning up resources - Run directory: {run_dir}, Error: {str(e)}'