SIENTIAPDE-1273

Enhance data handling and export processes in Laborious workflows

- Updated `gates.py` to improve data quality validation, filtering, and formatting operations, including enhanced metrics recording.
- Refined `mlflow.py` to better manage model transformations and reference data retrieval from MLflow Model Registry.
- Enhanced `format_and_export_prediction.py` to support separate export of transformed data, improving flexibility in data handling.
- Added comprehensive test coverage for new functionalities, including transformed data formatting and retrain report generation.
- Improved documentation in `README.md` to reflect changes in activities and workflows, ensuring clarity on data processing and export paths.

laborious/activities/mlflow.py

@@ -424,14 +424,30 @@ class MLFlow(SientiaMonitoring):
         """
         Get reference data from the MLflow Model Registry.
 
+        This method retrieves evaluation reference data stored as artifacts in the
+        MLflow Model Registry. The reference data is typically used for model
+        drift detection, performance comparison, and quality validation. The method
+        loads the data from a CSV artifact file and formats timestamps for
+        consistent processing.
+
+        The method handles:
+        1. Loading evaluation data artifact from MLflow Model Registry
+        2. Timestamp parsing and formatting for consistency
+        3. Data conversion to dictionary format for workflow consumption
+        4. Graceful handling of missing reference data
+
         Args:
             input_data (dict): Input data containing:
                 - metadata (dict): Workflow execution metadata
                 - model_name (str): Name of the MLFlow model to get reference data from
 
         Returns:
-            list[dict[Hashable, Any]] | None: Reference data from the MLflow Model Registry.
+            list[dict[Hashable, Any]] | None: Reference data from the MLflow Model Registry
+                as a list of dictionaries. Returns None if reference data is not found
+                or if the artifact does not exist.
 
+        Raises:
+            Exception: If artifact loading fails or encounters errors during processing
         """
 
         metadata = input_data['metadata']