SIENTIAPDE-1273

Enhance MLFlowRepository and Activities classes with new methods and metrics

- Added `check_artifact_exists` method to MLFlowRepository for verifying artifact presence in the MLflow Model Registry.
- Implemented `get_prediction_data` method in MLFlowRepository to retrieve prediction data from models.
- Updated Activities class to integrate ModelMetrics for improved metrics handling.
- Enhanced tests for artifact existence checks and prediction data retrieval, ensuring robust coverage for new functionalities.
- Updated various workflows to include `transform_table_name` in input data for better data handling.

laborious/worker/worker.py

@@ -29,6 +29,7 @@ from temporalio import client, workflow
 from temporalio.runtime import PrometheusConfig, Runtime, TelemetryConfig
 from temporalio.worker import PollerBehaviorAutoscaling, Worker
 
+
 with workflow.unsafe.imports_passed_through():
     import asyncio
     import os
@@ -49,6 +50,7 @@ with workflow.unsafe.imports_passed_through():
     )
     from laborious.workflows.minimal_retrain import MinimalRetrain
     from laborious.workflows.predictions_batch import PredictionsBatch
+    from laborious.workflows.drift import Drift
     from laborious.workflows.sub_workflows.format_and_export_prediction import (
         FormatAndExportPrediction,
     )
@@ -156,6 +158,23 @@ async def main():
             workflow_task_poller_behavior=PollerBehaviorAutoscaling(),
             activity_task_poller_behavior=PollerBehaviorAutoscaling(),
         ),
+        Worker(
+            temporal_client,
+            task_queue='drift-queue',
+            workflows=[Drift],
+            activities=[
+                activities.load_custom_query,
+                activities.get_reference_data,
+                activities.calculate_drift,
+                activities.export_data_to_postgres,
+            ],
+            max_concurrent_workflow_tasks=50,
+            max_concurrent_activities=50,
+            max_concurrent_local_activities=50,
+            max_cached_workflows=2,
+            workflow_task_poller_behavior=PollerBehaviorAutoscaling(),
+            activity_task_poller_behavior=PollerBehaviorAutoscaling(),
+        ),
         Worker(
             temporal_client,
             task_queue='predictions_batch-queue',
@@ -168,6 +187,7 @@ async def main():
                 activities.input_gate,
                 activities.mlflow_response_gate,
                 activities.mlflow_content_gate,
+                activities.format_transformed_data,
                 activities.format_prediction,
                 activities.format_default_prediction,
                 activities.get_last_timestamp,