SIENTIAPDE-1646

Implement new scheduling configurations for model retraining and drift analysis in input_sample.json

- Added three new schedule configurations: `minimal-retrain-test-runtime`, `drift-test-runtime`, and `simple-metrics-test-runtime`.
- Each configuration includes parameters such as model ID, workflow type, frequency, and specific queries for data retrieval.
- Enhanced the structure to support active status and updated timestamps for better tracking of schedule states.

laborious/activities/mlflow.py

@@ -324,8 +324,9 @@ class MLFlow(SientiaMonitoring):
 
         The activity normalizes ``NaN`` to ``None`` for JSON-friendly columns, sets the row index
         the same way as ``retrain_model`` (UTC ``DatetimeIndex`` from ``DATETIME_FORMAT_WITH_TZ``),
-        restores that index on the prediction frame, and records ``response_time``. Non-DataFrame
-        predictions are coerced to a single ``prediction`` column.
+        restores that index on the prediction frame, normalizes the prediction index to
+        ``DATETIME_FORMAT_WITH_TZ`` strings like ``request_transform``, and records ``response_time``.
+        Non-DataFrame predictions are coerced to a single ``prediction`` column.
 
         Args:
          - input_data: Same envelope as ``request_transform`` (``metadata``, ``model_name``,
@@ -381,6 +382,7 @@ class MLFlow(SientiaMonitoring):
 
             predict_data.index = input_index
             predict_data['response_time'] = (end_time - start_time).total_seconds()
+            predict_data = self._detect_and_parse_datetime_index(predict_data, metadata)
 
             response_data: dict[str, Any] = {'success': True, 'content': predict_data}
         except Exception as e: