Method: googleapis.aiplatform.v1.projects.locations.modelDeploymentMonitoringJobs.patch

Updates a ModelDeploymentMonitoringJob.

This method waits—the workflow execution is paused—until the operation is complete, fails, or times out. The default timeout value is 1800 seconds (30 minutes) and can be changed to a maximum value of 31536000 seconds (one year) for long-running operations using the connector_params field. See the Connectors reference.

The connector uses polling to monitor the long-running operation, which might generate additional billable steps. For more information about retries and long-running operations, refer to Understand connectors.

The polling policy for the long-running operation can be configured. To set the connector-specific parameters (connector_params), refer to Invoke a connector call.

Arguments

Parameters
`name`	`string` Required. Output only. Resource name of a ModelDeploymentMonitoringJob.
`updateMask`	`string (FieldMask format)` 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`
`region`	`string` Required. Region of the HTTP endpoint. For example, if region is set to `us-central1`, the endpoint https://us-central1-integrations.googleapis.com will be used. See service endpoints.
`body`	`object (GoogleCloudAiplatformV1ModelDeploymentMonitoringJob)` Required.

Raised exceptions

Exceptions
`ConnectionError`	In case of a network problem (such as DNS failure or refused connection).
`HttpError`	If the response status is >= 400 (excluding 429 and 503).
`TimeoutError`	If a long-running operation takes longer to finish than the specified timeout limit.
`TypeError`	If an operation or function receives an argument of the wrong type.
`ValueError`	If an operation or function receives an argument of the right type but an inappropriate value. For example, a negative timeout.
`OperationError`	If the long-running operation finished unsuccessfully.
`ResponseTypeError`	If the long-running operation returned a response of the wrong type.

Response

If successful, the response contains an instance of GoogleLongrunningOperation.

Subworkflow snippet

Some fields might be optional or required. To identify required fields, refer to the API documentation.

YAML

- patch:
    call: googleapis.aiplatform.v1.projects.locations.modelDeploymentMonitoringJobs.patch
    args:
        name: ...
        updateMask: ...
        region: ...
        body:
            analysisInstanceSchemaUri: ...
            displayName: ...
            enableMonitoringPipelineLogs: ...
            encryptionSpec:
                kmsKeyName: ...
            endpoint: ...
            labels: ...
            logTtl: ...
            loggingSamplingStrategy:
                randomSampleConfig:
                    sampleRate: ...
            modelDeploymentMonitoringObjectiveConfigs: ...
            modelDeploymentMonitoringScheduleConfig:
                monitorInterval: ...
                monitorWindow: ...
            modelMonitoringAlertConfig:
                emailAlertConfig:
                    userEmails: ...
                enableLogging: ...
                notificationChannels: ...
            predictInstanceSchemaUri: ...
            samplePredictInstance: ...
            statsAnomaliesBaseDirectory:
                outputUriPrefix: ...
    result: patchResult

JSON

[
  {
    "patch": {
      "call": "googleapis.aiplatform.v1.projects.locations.modelDeploymentMonitoringJobs.patch",
      "args": {
        "name": "...",
        "updateMask": "...",
        "region": "...",
        "body": {
          "analysisInstanceSchemaUri": "...",
          "displayName": "...",
          "enableMonitoringPipelineLogs": "...",
          "encryptionSpec": {
            "kmsKeyName": "..."
          },
          "endpoint": "...",
          "labels": "...",
          "logTtl": "...",
          "loggingSamplingStrategy": {
            "randomSampleConfig": {
              "sampleRate": "..."
            }
          },
          "modelDeploymentMonitoringObjectiveConfigs": "...",
          "modelDeploymentMonitoringScheduleConfig": {
            "monitorInterval": "...",
            "monitorWindow": "..."
          },
          "modelMonitoringAlertConfig": {
            "emailAlertConfig": {
              "userEmails": "..."
            },
            "enableLogging": "...",
            "notificationChannels": "..."
          },
          "predictInstanceSchemaUri": "...",
          "samplePredictInstance": "...",
          "statsAnomaliesBaseDirectory": {
            "outputUriPrefix": "..."
          }
        }
      },
      "result": "patchResult"
    }
  }
]