feat: log regression metrics as parameters in Training class

- Added a method to persist computed regression metrics (MSE, MAE, R²) as MLflow parameters during model training, enhancing model evaluation and tracking.
- Updated the Training class to log the equation path if available, improving artifact management.

model_manager/activities/training.py

@@ -14,7 +14,6 @@ with workflow.unsafe.imports_passed_through():
     from typing import Any
 
     import mlflow
-    import pandas as pd
     from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
     from sientia_do.notifications.models import NotificationLevel
     from sientia_do.observability.logger import Logger
@@ -340,10 +339,30 @@ class Training(SientiaMonitoring):
         self.info(f'Storing model for {train_params.model_type}', metadata)
         wrapper._input_example = None
         wrapper.store_model(name=train_params.model_name)
+        self._log_regression_metrics_as_params(train_result)
         self.info(f'Logging artifacts for {train_params.model_type}', metadata)
         mlflow.log_artifact(train_result.report_path)
         mlflow.log_artifact(train_result.train_data_path)
         mlflow.log_artifact(train_result.test_data_path)
+        if train_result.equation_path is not None:
+            mlflow.log_artifact(train_result.equation_path)
+
+    def _log_regression_metrics_as_params(self, train_result: TrainModelResult) -> None:
+        """
+        Persist computed regression metrics as MLflow params.
+
+        Args:
+            train_result: Training output containing computed regression metrics.
+        """
+        metric_params = {
+            'mse_val': train_result.mse_val,
+            'mae_val': train_result.mae_val,
+            'r2_val': train_result.r2_val,
+        }
+
+        for key, value in metric_params.items():
+            if value is not None:
+                mlflow.log_param(key, value)
 
     @activity.defn(name='cleanup_resources')
     def cleanup_resources(self, input_data: dict[str, Any]) -> None: