SIENTIAPDE-1231

SIENTIAPDE-1222
Enhance MLFlow and MLFlowRepository with model configuration support

- Introduced `model_config` parameter in MLFlow methods to streamline model handling and configuration management.
- Updated `retrain_model`, `transform`, and `predict` methods to accept `model_config` and `metadata` for improved flexibility and logging.
- Added `detect_and_parse_datetime_index` method to handle datetime index parsing with enhanced error handling and logging.
- Refactored model experiment creation to include transformation and prediction flavors, along with compression options.
- Improved documentation and type hints across methods for better clarity and usability.

laborious/activities/mlflow.py

@@ -225,6 +225,7 @@ class MLFlow(BaseActivity):
         metadata = input_data['metadata']
         data = DataFrame(input_data['data'])
         model_name = input_data['model_name']
+        model_config = input_data.get('model_config', {})
 
         self.info(f'Retraining model {model_name}...', metadata)
 
@@ -245,7 +246,8 @@ class MLFlow(BaseActivity):
         try:
             retrain_output, experiment = self.model_monitoring_repository.retrain_model(
                 data=data,
-                model_name=model_name
+                model_name=model_name,
+                model_config=model_config
             )
 
             return {