feat: add experiment_name to TrainModelResult and update training logic

- Introduced experiment_name parameter in TrainModelResult to enhance tracking of training experiments.
- Updated the Training class to utilize run_name and experiment_name for improved MLflow run management.

model_manager/activities/training.py

@@ -283,17 +283,17 @@ class Training(SientiaMonitoring):
             self.info(f'Starting MLflow run for {train_params.model_type}', metadata)
             with self.mlflow_repository.start_run(
                 model_name=train_params.model_name,
-                run_name=None,
-                experiment_name=f'{train_params.model_name}_experiment',
+                run_name=train_result.run_name,
+                experiment_name=train_result.experiment_name,
                 tags=None,
                 metadata=metadata,
             ) as run_info:
-                train_result.run_name = run_info.run_name
                 train_result.run_id = run_info.run_id
                 self._persist_training_artifacts(train_result, train_params, wrapper, metadata)
 
             return {
                 'run_name': train_result.run_name,
+                'experiment_name': train_result.experiment_name,
                 'run_id': train_result.run_id,
                 'run_dir': train_result.run_dir,
             }