gcloud-help/gcloud/ai/model-monitoring-jobs/create

NAME
    gcloud ai model-monitoring-jobs create - create a new Vertex AI model
        monitoring job

SYNOPSIS
    gcloud ai model-monitoring-jobs create --display-name=DISPLAY_NAME
        --emails=[EMAILS,...] --endpoint=ENDPOINT
        --prediction-sampling-rate=PREDICTION_SAMPLING_RATE
        [--analysis-instance-schema=ANALYSIS_INSTANCE_SCHEMA]
        [--[no-]anomaly-cloud-logging] [--labels=[KEY=VALUE,...]]
        [--log-ttl=LOG_TTL]
        [--monitoring-frequency=MONITORING_FREQUENCY; default=24]
        [--notification-channels=[NOTIFICATION_CHANNELS,...]]
        [--predict-instance-schema=PREDICT_INSTANCE_SCHEMA] [--region=REGION]
        [--sample-predict-request=SAMPLE_PREDICT_REQUEST]
        [--kms-key=KMS_KEY : --kms-keyring=KMS_KEYRING
          --kms-location=KMS_LOCATION --kms-project=KMS_PROJECT]
        [--monitoring-config-from-file=MONITORING_CONFIG_FROM_FILE
          | --feature-attribution-thresholds=[KEY=VALUE,...]
          --feature-thresholds=[KEY=VALUE,...] --target-field=TARGET_FIELD
          --training-sampling-rate=TRAINING_SAMPLING_RATE;
          default=1.0 --bigquery-uri=BIGQUERY_URI | --dataset=DATASET
          | --data-format=DATA_FORMAT --gcs-uris=[GCS_URIS,...]]
        [GCLOUD_WIDE_FLAG ...]

DESCRIPTION
    Create a new Vertex AI model monitoring job.

EXAMPLES
    To create a model deployment monitoring job under project example in region
    us-central1 for endpoint 123, run:

        $ gcloud ai model-monitoring-jobs create --project=example \
            --region=us-central1 --display-name=my_monitoring_job \
            --emails=a@gmail.com,b@gmail.com --endpoint=123 \
            --prediction-sampling-rate=0.2

    To create a model deployment monitoring job with drift detection for all
    the deployed models under the endpoint 123, run:

        $ gcloud ai model-monitoring-jobs create --project=example \
            --region=us-central1 --display-name=my_monitoring_job \
            --emails=a@gmail.com,b@gmail.com --endpoint=123 \
            --prediction-sampling-rate=0.2 \
            --feature-thresholds=feat1=0.1,feat2=0.2,feat3=0.2,feat4=0.3

    To create a model deployment monitoring job with skew detection for all the
    deployed models under the endpoint 123, with training dataset from Google
    Cloud Storage, run:

        $ gcloud ai model-monitoring-jobs create --project=example \
            --region=us-central1 --display-name=my_monitoring_job \
            --emails=a@gmail.com,b@gmail.com --endpoint=123 \
            --prediction-sampling-rate=0.2 \
            --feature-thresholds=feat1=0.1,feat2=0.2,feat3=0.2,feat4=0.3 \
            --target-field=price --data-format=csv \
            --gcs-uris=gs://test-bucket/dataset.csv

    To create a model deployment monitoring job with skew detection for all the
    deployed models under the endpoint 123, with training dataset from Vertex
    AI dataset 456, run:

        $ gcloud ai model-monitoring-jobs create --project=example \
            --region=us-central1 --display-name=my_monitoring_job \
            --emails=a@gmail.com,b@gmail.com --endpoint=123 \
            --prediction-sampling-rate=0.2 \
            --feature-thresholds=feat1=0.1,feat2=0.2,feat3=0.2,feat4=0.3 \
            --target-field=price --dataset=456

    To create a model deployment monitoring job with different drift detection
    or skew detection for different deployed models, run:

        $ gcloud ai model-monitoring-jobs create --project=example \
            --region=us-central1 --display-name=my_monitoring_job \
            --emails=a@gmail.com,b@gmail.com --endpoint=123 \
            --prediction-sampling-rate=0.2 \
            --monitoring-config-from-file=your_objective_config.yaml

    After creating the monitoring job, be sure to send some predict requests.
    It will be used to generate some metadata for analysis purpose, like
    predict and analysis instance schema.

REQUIRED FLAGS
     --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

     --endpoint=ENDPOINT
        Id of the endpoint.

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

OPTIONAL 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.

     --labels=[KEY=VALUE,...]
        List of label KEY=VALUE pairs to add.

        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.

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

     --monitoring-frequency=MONITORING_FREQUENCY; default=24
        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

     --predict-instance-schema=PREDICT_INSTANCE_SCHEMA
        YAML schema file uri(Google Cloud Storage) describing the format of a
        single instance, which are given to format this Endpoint's prediction.
        If not set, predict schema will be generated from collected predict
        requests.

     Region resource - Cloud region to create model deployment monitoring job.
     This represents a Cloud 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 --region on the command line with a fully
        specified name;
      ◆ set the property ai/region with a fully specified name;
      ◆ choose one from the prompted list of available regions with a fully
        specified name;
      ◆ provide the argument --project on the command line;
      ◆ set the property core/project.

     --region=REGION
        ID of the region or fully qualified identifier for the region.

        To set the region attribute:
        ◆ provide the argument --region on the command line;
        ◆ set the property ai/region;
        ◆ choose one from the prompted list of available regions.

     --sample-predict-request=SAMPLE_PREDICT_REQUEST
        Path to a local file containing the body of a JSON object. Same format
        as [PredictRequest.instances][], this can be set as a replacement of
        predict-instance-schema. If not set, predict schema will be generated
        from collected predict requests.

        An example of a JSON request:

            {"x": [1, 2], "y": [3, 4]}

     Key resource - The Cloud KMS (Key Management Service) cryptokey that will
     be used to protect the model deployment monitoring job. The 'Vertex AI
     Service Agent' service account must hold permission 'Cloud KMS CryptoKey
     Encrypter/Decrypter'. The arguments in this group can be used to specify
     the attributes of this resource.

     --kms-key=KMS_KEY
        ID of the key or fully qualified identifier for the key.

        To set the kms-key attribute:
        ◆ provide the argument --kms-key on the command line.

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

     --kms-keyring=KMS_KEYRING
        The KMS keyring of the key.

        To set the kms-keyring attribute:
        ◆ provide the argument --kms-key on the command line with a fully
          specified name;
        ◆ provide the argument --kms-keyring on the command line.

     --kms-location=KMS_LOCATION
        The Google Cloud location for the key.

        To set the kms-location attribute:
        ◆ provide the argument --kms-key on the command line with a fully
          specified name;
        ◆ provide the argument --kms-location on the command line.

     --kms-project=KMS_PROJECT
        The Google Cloud project for the key.

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

     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

         --target-field=TARGET_FIELD
            Target field name the model is to predict. Must be provided if
            you'd like to do training-prediction skew detection.

         --training-sampling-rate=TRAINING_SAMPLING_RATE; default=1.0
            Training Dataset sampling rate.

         At most one of these can be specified:

           --bigquery-uri=BIGQUERY_URI
              BigQuery table of the unmanaged Dataset used to train this Model.
              For example: bq://projectId.bqDatasetId.bqTableId.

           --dataset=DATASET
              Id of Vertex AI Dataset used to train this Model.

           Or at least one of these can be specified:

             --data-format=DATA_FORMAT
                Data format of the dataset, must be provided if the input is
                from Google Cloud Storage. The possible formats are: tf-record,
                csv

             --gcs-uris=[GCS_URIS,...]
                Comma-separated Google Cloud Storage uris of the unmanaged
                Datasets used to train this Model.

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 create

        $ gcloud beta ai model-monitoring-jobs create