gcloud beta ai model-monitoring-jobs update

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

gcloud beta 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 …]

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.

--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 beta ai model-monitoring-jobs update --clear-labels

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

gcloud beta 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

--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

Run $ gcloud help for details.