SIENTIAPDE-1325

Refactor monitoring and metrics integration across various components

- Removed coverage options from `pyproject.toml`.
- Updated prediction metrics in `README.md` to replace `pipeline_name` with `workflow_name`.
- Upgraded `sientia-dataops-library` dependency version in `requirements-light.txt` and `requirements.txt`.
- Enhanced metrics handling in `laborious` activities, including `Activities`, `Gates`, `MLFlow`, and `OPC`, to utilize a new `MetricsController`.
- Refactored metric emission methods to improve clarity and consistency across the codebase.
- Updated tests to reflect changes in metrics handling and ensure proper functionality.

tests/laborious/activities/test_mlflow.py

@@ -1,4 +1,4 @@
-from unittest.mock import ANY, MagicMock, call, patch
+from unittest.mock import ANY, AsyncMock, MagicMock, call, patch
 
 import numpy as np
 from pytest import fixture, mark, raises
@@ -25,6 +25,7 @@ def test___init__(mock_minio_repository, mock_mlflow_repository):
         },
         logger=MagicMock(),
         notification_handler=MagicMock(),
+        metrics_controller=AsyncMock(),
     )
 
     assert mlflow.mlflow_host == 'http://localhost'
@@ -32,7 +33,9 @@ def test___init__(mock_minio_repository, mock_mlflow_repository):
     assert mlflow.mlflow_username == 'admin'
     assert mlflow.mlflow_password == 'admin'
 
-    mock_mlflow_repository.assert_called_once_with('http://localhost:5000', 'admin', 'admin', ANY)
+    mock_mlflow_repository.assert_called_once_with(
+        'http://localhost:5000', 'admin', 'admin', ANY, ANY, ANY
+    )
 
     mock_minio_repository.assert_called_once_with(
         logger=ANY,
@@ -42,6 +45,7 @@ def test___init__(mock_minio_repository, mock_mlflow_repository):
         minio_secret_key='minio123',
         minio_region_name='us-east-1',
         minio_default_bucket='test',
+        metrics_controller=ANY,
     )
 
 
@@ -63,9 +67,15 @@ def mlflow(mock_minio_repository, mock_mlflow_repository):
         },
         logger=MagicMock(),
         notification_handler=MagicMock(),
+        metrics_controller=AsyncMock(),
     )
 
+    mlflow.model_monitoring_repository = AsyncMock()
+    mlflow.minio_repository = AsyncMock()
+
     mlflow.send_notification = MagicMock()
+    mlflow.emit_metric = AsyncMock()
+    mlflow.send_notification_async = AsyncMock()
 
     return mlflow
 
@@ -225,6 +235,8 @@ async def test_retrain_model_success_data_success_retrain(mock_to_datetime, mlfl
         'message': 'Model retrained successfully.',
     }
 
+    mlflow.minio_repository.get_parquet_as_dataframe.return_value = MagicMock()
+
     response = await mlflow.retrain_model(
         {
             **metadata,
@@ -301,6 +313,8 @@ async def test_retrain_model_success_data_fail_retrain(mock_to_datetime, mlflow)
         'message': 'Model retrained failed.',
     }
 
+    mlflow.minio_repository.get_parquet_as_dataframe.return_value = MagicMock()
+
     response = await mlflow.retrain_model(
         {
             **metadata,
@@ -360,7 +374,7 @@ async def test_retrain_model_success_data_fail_retrain(mock_to_datetime, mlflow)
         metadata=metadata['metadata'],
     )
 
-    mlflow.send_notification.assert_called_once_with(
+    mlflow.send_notification_async.assert_called_once_with(
         metadata=metadata['metadata'],
         notification_id='RETRAIN_MODEL_ERROR',
         message='Error retraining model test_model: Model retrained failed.',
@@ -464,7 +478,7 @@ async def test_update_production_model_error(mlflow):
         await mlflow.update_production_model(input_data)
     except Exception as e:
         assert str(e) == 'Error updating production model'
-        mlflow.send_notification.assert_called_once_with(
+        mlflow.send_notification_async.assert_called_once_with(
             metadata=metadata['metadata'],
             notification_id='UPDATE_PRODUCTION_MODEL_ERROR',
             message='Error updating production model test_model: Error updating production model',