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/utils/models/train_model_params.py

@@ -84,6 +84,8 @@ class TrainModelParams:
         Args:
             data: Dictionary containing training parameters with keys matching the
                   attribute names (e.g. variable_columns, data_model_kwargs, model_kwargs, opt_params).
+                  model_metadata may be omitted or None until load_model_metadata fills it.
+                  experiment_run_id may be an int or numeric string.
 
         Returns:
             TrainModelParams: Validated instance with all fields populated
@@ -111,7 +113,7 @@ class TrainModelParams:
             train_size=cls._check_none(data.get('train_size'), int, 'train_size'),
             shuffle=cls._check_none(data.get('shuffle'), bool, 'shuffle'),
             random_state=cls._check_none(data.get('random_state', 42), int, 'random_state'),
-            experiment_run_id=cls._check_none(data.get('experiment_run_id'), int, 'experiment_run_id'),
+            experiment_run_id=cls._coerce_experiment_run_id(data.get('experiment_run_id')),
             model_name=model_name,
             experiment_name=model_name + '_experiment',
             val_file_name=data.get('val_file_name'),
@@ -121,7 +123,7 @@ class TrainModelParams:
             model_type=cls._check_none(data.get('model_type'), str, 'model_type'),
             model_id=data.get('model_id'),
             
-            model_metadata=cls._check_none(data.get('model_metadata'), dict, 'model_metadata'),
+            model_metadata=cls._parse_optional_model_metadata(data.get('model_metadata')),
         )
 
     def to_dict(self) -> dict[str, Any]:
@@ -181,6 +183,60 @@ class TrainModelParams:
 
         return value
 
+    @staticmethod
+    def _coerce_experiment_run_id(value: Any) -> int:
+        """
+        Coerce experiment_run_id to int.
+
+        Workflow clients may send numeric strings; this keeps from_dict aligned with
+        workflow validation.
+
+        Args:
+            value: Raw experiment_run_id from the payload.
+
+        Returns:
+            int: Parsed experiment run id.
+
+        Raises:
+            ValueError: If the value is None.
+            TypeError: If the value cannot be coerced to a non-boolean integer.
+        """
+        if value is None:
+            raise ValueError('experiment_run_id is required and cannot be None.')
+        if isinstance(value, bool):
+            raise TypeError('experiment_run_id must be an integer, got bool.')
+        if isinstance(value, int):
+            return value
+        if isinstance(value, str) and value.strip().isdigit():
+            return int(value.strip())
+        if isinstance(value, float) and value.is_integer():
+            return int(value)
+        raise TypeError(
+            f'experiment_run_id must be an integer or numeric string, but got {type(value).__name__}.'
+        )
+
+    @staticmethod
+    def _parse_optional_model_metadata(value: Any) -> dict | None:
+        """
+        Parse model_metadata for from_dict before load_model_metadata fills the index.
+
+        Args:
+            value: model_metadata from the payload, or None if not sent yet.
+
+        Returns:
+            dict | None: Dict when provided; None when absent (filled later by load_model_metadata).
+
+        Raises:
+            TypeError: If value is neither None nor a dict.
+        """
+        if value is None:
+            return None
+        if isinstance(value, dict):
+            return value
+        raise TypeError(
+            f'model_metadata must be a dict or None, but got {type(value).__name__}.'
+        )
+
     def validate_business_rules(self) -> None:
         """
         Validate business rules and constraints for training parameters.