NAME
    gcloud ai model-monitoring-jobs update - update an Vertex AI model
        deployment monitoring job

SYNOPSIS
    gcloud ai model-monitoring-jobs update (MONITORING_JOB : --region=REGION)
        [--analysis-instance-schema=ANALYSIS_INSTANCE_SCHEMA]
        [--[no-]anomaly-cloud-logging] [--display-name=DISPLAY_NAME]
        [--emails=[EMAILS,...]] [--log-ttl=LOG_TTL]
        [--monitoring-frequency=MONITORING_FREQUENCY]
        [--notification-channels=[NOTIFICATION_CHANNELS,...]]
        [--prediction-sampling-rate=PREDICTION_SAMPLING_RATE]
        [--update-labels=[KEY=VALUE,...]]
        [--clear-labels | --remove-labels=[KEY,...]]
        [--monitoring-config-from-file=MONITORING_CONFIG_FROM_FILE
          | --feature-attribution-thresholds=[KEY=VALUE,...]
          --feature-thresholds=[KEY=VALUE,...]] [GCLOUD_WIDE_FLAG ...]

DESCRIPTION
    Update an Vertex AI model deployment monitoring job.

EXAMPLES
    To update display name of model deployment monitoring job 123 under project
    example in region us-central1, run:

        $ gcloud ai model-monitoring-jobs update 123 \
            --display-name=new-name --project=example --region=us-central1

POSITIONAL ARGUMENTS
     Monitoring job resource - The model deployment monitoring job to update.
     The arguments in this group can be used to specify the attributes of this
     resource. (NOTE) Some attributes are not given arguments in this group but
     can be set in other ways.

     To set the project attribute:
      ◆ provide the argument monitoring_job on the command line with a fully
        specified name;
      ◆ provide the argument --project on the command line;
      ◆ set the property core/project.

     This must be specified.

       MONITORING_JOB
          ID of the monitoring_job or fully qualified identifier for the
          monitoring_job.

          To set the name attribute:
          ▸ provide the argument monitoring_job on the command line.

          This positional argument must be specified if any of the other
          arguments in this group are specified.

       --region=REGION
          Cloud region for the monitoring_job.

          To set the region attribute:
          ▸ provide the argument monitoring_job on the command line with a
            fully specified name;
          ▸ provide the argument --region on the command line;
          ▸ set the property ai/region;
          ▸ choose one from the prompted list of available regions.

FLAGS
     --analysis-instance-schema=ANALYSIS_INSTANCE_SCHEMA
        YAML schema file uri(Google Cloud Storage) describing the format of a
        single instance that you want Tensorflow Data Validation (TFDV) to
        analyze.

     --[no-]anomaly-cloud-logging
        If true, anomaly will be sent to Cloud Logging. Use
        --anomaly-cloud-logging to enable and --no-anomaly-cloud-logging to
        disable.

     --display-name=DISPLAY_NAME
        Display name of the model deployment monitoring job.

     --emails=[EMAILS,...]
        Comma-separated email address list. e.g.
        --emails=a@gmail.com,b@gmail.com

     --log-ttl=LOG_TTL
        TTL of BigQuery tables in user projects which stores logs(Day-based
        unit).

     --monitoring-frequency=MONITORING_FREQUENCY
        Monitoring frequency, unit is 1 hour.

     --notification-channels=[NOTIFICATION_CHANNELS,...]
        Comma-separated notification channel list. e.g.
        --notification-channels=projects/fake-project/notificationChannels/123,projects/fake-project/notificationChannels/456

     --prediction-sampling-rate=PREDICTION_SAMPLING_RATE
        Prediction sampling rate.

     --update-labels=[KEY=VALUE,...]
        List of label KEY=VALUE pairs to update. If a label exists, its value
        is modified. Otherwise, a new label is created.

        Keys must start with a lowercase character and contain only hyphens
        (-), underscores (_), lowercase characters, and numbers. Values must
        contain only hyphens (-), underscores (_), lowercase characters, and
        numbers.

     At most one of these can be specified:

       --clear-labels
          Remove all labels. If --update-labels is also specified then
          --clear-labels is applied first.

          For example, to remove all labels:

              $ gcloud ai model-monitoring-jobs update --clear-labels

          To remove all existing labels and create two new labels, foo and baz:

              $ gcloud ai model-monitoring-jobs update --clear-labels \
                --update-labels foo=bar,baz=qux

       --remove-labels=[KEY,...]
          List of label keys to remove. If a label does not exist it is
          silently ignored. If --update-labels is also specified then
          --update-labels is applied first.

     At most one of these can be specified:

       --monitoring-config-from-file=MONITORING_CONFIG_FROM_FILE
          Path to the model monitoring objective config file. This file should
          be a YAML document containing a
          ModelDeploymentMonitoringJob(https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.modelDeploymentMonitoringJobs#ModelDeploymentMonitoringJob),
          but only the ModelDeploymentMonitoringObjectiveConfig needs to be
          configured.

          Note: Only one of --monitoring-config-from-file and other objective
          config set, like --feature-thresholds,
          --feature-attribution-thresholds needs to be set.

          Example(YAML):

              modelDeploymentMonitoringObjectiveConfigs:
              - deployedModelId: '5251549009234886656'
                objectiveConfig:
                  trainingDataset:
                    dataFormat: csv
                    gcsSource:
                      uris:
                      - gs://fake-bucket/training_data.csv
                    targetField: price
                  trainingPredictionSkewDetectionConfig:
                    skewThresholds:
                      feat1:
                        value: 0.9
                      feat2:
                        value: 0.8
              - deployedModelId: '2945706000021192704'
                objectiveConfig:
                  predictionDriftDetectionConfig:
                    driftThresholds:
                      feat1:
                        value: 0.3
                      feat2:
                        value: 0.4

       Or at least one of these can be specified:

         --feature-attribution-thresholds=[KEY=VALUE,...]
            List of feature-attribution score threshold value pairs(Apply for
            all the deployed models under the endpoint, if you want to specify
            different thresholds for different deployed model, please use flag
            --monitoring-config-from-file or call API directly). If only
            feature name is set, the default threshold value would be 0.3.

            For example:
            feature-attribution-thresholds=feat1=0.1,feat2,feat3=0.2

         --feature-thresholds=[KEY=VALUE,...]
            List of feature-threshold value pairs(Apply for all the deployed
            models under the endpoint, if you want to specify different
            thresholds for different deployed model, please use flag
            --monitoring-config-from-file or call API directly). If only
            feature name is set, the default threshold value would be 0.3.

            For example: --feature-thresholds=feat1=0.1,feat2,feat3=0.2

GCLOUD WIDE FLAGS
    These flags are available to all commands: --access-token-file, --account,
    --billing-project, --configuration, --flags-file, --flatten, --format,
    --help, --impersonate-service-account, --log-http, --project, --quiet,
    --trace-token, --user-output-enabled, --verbosity.

    Run $ gcloud help for details.

NOTES
    These variants are also available:

        $ gcloud alpha ai model-monitoring-jobs update

        $ gcloud beta ai model-monitoring-jobs update