feat: enhance training and experiment tracking functionality

- Updated `Activities` class to improve garbage collection handling.
- Enhanced error messaging in `ExperimentTracking` for better clarity on update failures.
- Refactored `Training` class to streamline exception handling and improve type hints.
- Introduced new methods in `TrainModelParams` for better handling of experiment run IDs and model metadata.
- Added functionality to extract model equations in `DataManagerRepository` for linear regression models.

model_manager/activities/experiment_tracking.py

@@ -260,7 +260,8 @@ class ExperimentTracking(Postgres):
 
             if result.get('rowcount', 0) == 0:
                 error_msg = (
-                    f'No rows updated for experiment run {experiment_run_id} with status {status}'
+                    f'No experiment_run row updated for id={experiment_run_id} '
+                    f'(row missing or id mismatch). update_type={update_type!r}, status={status!r}.'
                 )
                 raise ValueError(error_msg)