feat: add Vizier service to aiplatform v1 (#671)

* feat: add Vizier service to aiplatform v1

Committer: @dizcology
PiperOrigin-RevId: 394116440

Source-Link: googleapis/googleapis@67c780b

Source-Link: googleapis/googleapis-gen@994d9a5

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google/cloud/aiplatform_v1/services/job_service/async_client.py

@@ -2218,8 +2218,34 @@ async def update_model_deployment_monitoring_job(
                 on the ``request`` instance; if ``request`` is provided, this
                 should not be set.
             update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`):
-                Required. The update mask applies to
-                the resource.
+                Required. The update mask is used to specify the fields
+                to be overwritten in the ModelDeploymentMonitoringJob
+                resource by the update. The fields specified in the
+                update_mask are relative to the resource, not the full
+                request. A field will be overwritten if it is in the
+                mask. If the user does not provide a mask then only the
+                non-empty fields present in the request will be
+                overwritten. Set the update_mask to ``*`` to override
+                all fields. For the objective config, the user can
+                either provide the update mask for
+                model_deployment_monitoring_objective_configs or any
+                combination of its nested fields, such as:
+                model_deployment_monitoring_objective_configs.objective_config.training_dataset.
+
+                Updatable fields:
+
+                -  ``display_name``
+                -  ``model_deployment_monitoring_schedule_config``
+                -  ``model_monitoring_alert_config``
+                -  ``logging_sampling_strategy``
+                -  ``labels``
+                -  ``log_ttl``
+                -  ``enable_monitoring_pipeline_logs`` . and
+                -  ``model_deployment_monitoring_objective_configs`` .
+                   or
+                -  ``model_deployment_monitoring_objective_configs.objective_config.training_dataset``
+                -  ``model_deployment_monitoring_objective_configs.objective_config.training_prediction_skew_detection_config``
+                -  ``model_deployment_monitoring_objective_configs.objective_config.prediction_drift_detection_config``
 
                 This corresponds to the ``update_mask`` field
                 on the ``request`` instance; if ``request`` is provided, this

google/cloud/aiplatform_v1/services/job_service/client.py

@@ -2574,8 +2574,34 @@ def update_model_deployment_monitoring_job(
                 on the ``request`` instance; if ``request`` is provided, this
                 should not be set.
             update_mask (google.protobuf.field_mask_pb2.FieldMask):
-                Required. The update mask applies to
-                the resource.
+                Required. The update mask is used to specify the fields
+                to be overwritten in the ModelDeploymentMonitoringJob
+                resource by the update. The fields specified in the
+                update_mask are relative to the resource, not the full
+                request. A field will be overwritten if it is in the
+                mask. If the user does not provide a mask then only the
+                non-empty fields present in the request will be
+                overwritten. Set the update_mask to ``*`` to override
+                all fields. For the objective config, the user can
+                either provide the update mask for
+                model_deployment_monitoring_objective_configs or any
+                combination of its nested fields, such as:
+                model_deployment_monitoring_objective_configs.objective_config.training_dataset.
+
+                Updatable fields:
+
+                -  ``display_name``
+                -  ``model_deployment_monitoring_schedule_config``
+                -  ``model_monitoring_alert_config``
+                -  ``logging_sampling_strategy``
+                -  ``labels``
+                -  ``log_ttl``
+                -  ``enable_monitoring_pipeline_logs`` . and
+                -  ``model_deployment_monitoring_objective_configs`` .
+                   or
+                -  ``model_deployment_monitoring_objective_configs.objective_config.training_dataset``
+                -  ``model_deployment_monitoring_objective_configs.objective_config.training_prediction_skew_detection_config``
+                -  ``model_deployment_monitoring_objective_configs.objective_config.prediction_drift_detection_config``
 
                 This corresponds to the ``update_mask`` field
                 on the ``request`` instance; if ``request`` is provided, this

google/cloud/aiplatform_v1/types/job_service.py

@@ -791,8 +791,32 @@ class UpdateModelDeploymentMonitoringJobRequest(proto.Message):
             Required. The model monitoring configuration
             which replaces the resource on the server.
         update_mask (google.protobuf.field_mask_pb2.FieldMask):
-            Required. The update mask applies to the
-            resource.
+            Required. The update mask is used to specify the fields to
+            be overwritten in the ModelDeploymentMonitoringJob resource
+            by the update. The fields specified in the update_mask are
+            relative to the resource, not the full request. A field will
+            be overwritten if it is in the mask. If the user does not
+            provide a mask then only the non-empty fields present in the
+            request will be overwritten. Set the update_mask to ``*`` to
+            override all fields. For the objective config, the user can
+            either provide the update mask for
+            model_deployment_monitoring_objective_configs or any
+            combination of its nested fields, such as:
+            model_deployment_monitoring_objective_configs.objective_config.training_dataset.
+
+            Updatable fields:
+
+            -  ``display_name``
+            -  ``model_deployment_monitoring_schedule_config``
+            -  ``model_monitoring_alert_config``
+            -  ``logging_sampling_strategy``
+            -  ``labels``
+            -  ``log_ttl``
+            -  ``enable_monitoring_pipeline_logs`` . and
+            -  ``model_deployment_monitoring_objective_configs`` . or
+            -  ``model_deployment_monitoring_objective_configs.objective_config.training_dataset``
+            -  ``model_deployment_monitoring_objective_configs.objective_config.training_prediction_skew_detection_config``
+            -  ``model_deployment_monitoring_objective_configs.objective_config.prediction_drift_detection_config``
     """
 
     model_deployment_monitoring_job = proto.Field(