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

Creates a ModelDeploymentMonitoringJob. It will run periodically on a configured interval.

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
parent

string

Required. The parent of the ModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}
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 GoogleCloudAiplatformV1ModelDeploymentMonitoringJob.

Subworkflow snippet

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

YAML

- create:
    call: googleapis.aiplatform.v1.projects.locations.modelDeploymentMonitoringJobs.create
    args:
        parent: ...
        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: createResult

JSON

[
  {
    "create": {
      "call": "googleapis.aiplatform.v1.projects.locations.modelDeploymentMonitoringJobs.create",
      "args": {
        "parent": "...",
        "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": "createResult"
    }
  }
]