gcloud ai model-monitoring-jobs update - update an Vertex AI model deployment monitoring job
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] [--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 ...]
Update an Vertex AI model deployment monitoring job.
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
- 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.
- --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
- --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
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.
These variants are also available:
$ gcloud alpha ai model-monitoring-jobs update
$ gcloud beta ai model-monitoring-jobs update