
.TH "GCLOUD_AI_MODEL\-MONITORING\-JOBS_UPDATE" 1



.SH "NAME"
.HP
gcloud ai model\-monitoring\-jobs update \- update an Vertex AI model deployment monitoring job



.SH "SYNOPSIS"
.HP
\f5gcloud ai model\-monitoring\-jobs update\fR (\fIMONITORING_JOB\fR\ :\ \fB\-\-region\fR=\fIREGION\fR) [\fB\-\-analysis\-instance\-schema\fR=\fIANALYSIS_INSTANCE_SCHEMA\fR] [\fB\-\-[no\-]anomaly\-cloud\-logging\fR] [\fB\-\-display\-name\fR=\fIDISPLAY_NAME\fR] [\fB\-\-emails\fR=[\fIEMAILS\fR,...]] [\fB\-\-log\-ttl\fR=\fILOG_TTL\fR] [\fB\-\-monitoring\-frequency\fR=\fIMONITORING_FREQUENCY\fR] [\fB\-\-notification\-channels\fR=[\fINOTIFICATION_CHANNELS\fR,...]] [\fB\-\-prediction\-sampling\-rate\fR=\fIPREDICTION_SAMPLING_RATE\fR] [\fB\-\-update\-labels\fR=[\fIKEY\fR=\fIVALUE\fR,...]] [\fB\-\-clear\-labels\fR\ |\ \fB\-\-remove\-labels\fR=[\fIKEY\fR,...]] [\fB\-\-monitoring\-config\-from\-file\fR=\fIMONITORING_CONFIG_FROM_FILE\fR\ |\ \fB\-\-feature\-attribution\-thresholds\fR=[\fIKEY\fR=\fIVALUE\fR,...]\ \fB\-\-feature\-thresholds\fR=[\fIKEY\fR=\fIVALUE\fR,...]] [\fIGCLOUD_WIDE_FLAG\ ...\fR]



.SH "DESCRIPTION"

Update an Vertex AI model deployment monitoring job.



.SH "EXAMPLES"

To update display name of model deployment monitoring job \f5123\fR under
project \f5example\fR in region \f5us\-central1\fR, run:

.RS 2m
$ gcloud ai model\-monitoring\-jobs update 123 \e
    \-\-display\-name=new\-name \-\-project=example \-\-region=us\-central1
.RE



.SH "POSITIONAL ARGUMENTS"

.RS 2m
.TP 2m

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 \f5project\fR attribute:
.RS 2m
.IP "\(em" 2m
provide the argument \f5monitoring_job\fR on the command line with a fully
specified name;
.IP "\(em" 2m
provide the argument \f5\-\-project\fR on the command line;
.IP "\(em" 2m
set the property \f5core/project\fR.
.RE
.sp

This must be specified.


.RS 2m
.TP 2m
\fIMONITORING_JOB\fR

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

To set the \f5name\fR attribute:
.RS 2m
.IP "\(bu" 2m
provide the argument \f5monitoring_job\fR on the command line.
.RE
.sp

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

.TP 2m
\fB\-\-region\fR=\fIREGION\fR

Cloud region for the monitoring_job.

To set the \f5region\fR attribute:
.RS 2m
.IP "\(bu" 2m
provide the argument \f5monitoring_job\fR on the command line with a fully
specified name;
.IP "\(bu" 2m
provide the argument \f5\-\-region\fR on the command line;
.IP "\(bu" 2m
set the property \f5ai/region\fR;
.IP "\(bu" 2m
choose one from the prompted list of available regions.
.RE
.sp


.RE
.RE
.sp

.SH "FLAGS"

.RS 2m
.TP 2m
\fB\-\-analysis\-instance\-schema\fR=\fIANALYSIS_INSTANCE_SCHEMA\fR

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

.TP 2m
\fB\-\-[no\-]anomaly\-cloud\-logging\fR

If true, anomaly will be sent to Cloud Logging. Use
\fB\-\-anomaly\-cloud\-logging\fR to enable and
\fB\-\-no\-anomaly\-cloud\-logging\fR to disable.

.TP 2m
\fB\-\-display\-name\fR=\fIDISPLAY_NAME\fR

Display name of the model deployment monitoring job.

.TP 2m
\fB\-\-emails\fR=[\fIEMAILS\fR,...]

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

.TP 2m
\fB\-\-log\-ttl\fR=\fILOG_TTL\fR

TTL of BigQuery tables in user projects which stores logs(Day\-based unit).

.TP 2m
\fB\-\-monitoring\-frequency\fR=\fIMONITORING_FREQUENCY\fR

Monitoring frequency, unit is 1 hour.

.TP 2m
\fB\-\-notification\-channels\fR=[\fINOTIFICATION_CHANNELS\fR,...]

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

.TP 2m
\fB\-\-prediction\-sampling\-rate\fR=\fIPREDICTION_SAMPLING_RATE\fR

Prediction sampling rate.

.TP 2m
\fB\-\-update\-labels\fR=[\fIKEY\fR=\fIVALUE\fR,...]

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 (\f5\-\fR),
underscores (\f5_\fR), lowercase characters, and numbers. Values must contain
only hyphens (\f5\-\fR), underscores (\f5_\fR), lowercase characters, and
numbers.

.TP 2m

At most one of these can be specified:


.RS 2m
.TP 2m
\fB\-\-clear\-labels\fR

Remove all labels. If \f5\-\-update\-labels\fR is also specified then
\f5\-\-clear\-labels\fR is applied first.

For example, to remove all labels:

.RS 2m
$ gcloud ai model\-monitoring\-jobs update \-\-clear\-labels
.RE

To remove all existing labels and create two new labels, \f5\fIfoo\fR\fR and
\f5\fIbaz\fR\fR:

.RS 2m
$ gcloud ai model\-monitoring\-jobs update \-\-clear\-labels \e
  \-\-update\-labels foo=bar,baz=qux
.RE

.TP 2m
\fB\-\-remove\-labels\fR=[\fIKEY\fR,...]

List of label keys to remove. If a label does not exist it is silently ignored.
If \f5\-\-update\-labels\fR is also specified then \f5\-\-update\-labels\fR is
applied first.

.RE
.sp
.TP 2m

At most one of these can be specified:


.RS 2m
.TP 2m
\fB\-\-monitoring\-config\-from\-file\fR=\fIMONITORING_CONFIG_FROM_FILE\fR

Path to the model monitoring objective config file. This file should be a YAML
document containing a
\f5ModelDeploymentMonitoringJob\fR(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):

.RS 2m
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
.RE

.TP 2m

Or at least one of these can be specified:


.RS 2m
.TP 2m
\fB\-\-feature\-attribution\-thresholds\fR=[\fIKEY\fR=\fIVALUE\fR,...]

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: \f5feature\-attribution\-thresholds=feat1=0.1,feat2,feat3=0.2\fR

.TP 2m
\fB\-\-feature\-thresholds\fR=[\fIKEY\fR=\fIVALUE\fR,...]

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: \f5\-\-feature\-thresholds=feat1=0.1,feat2,feat3=0.2\fR


.RE
.RE
.RE
.sp

.SH "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 \fB$ gcloud help\fR for details.



.SH "NOTES"

These variants are also available:

.RS 2m
$ gcloud alpha ai model\-monitoring\-jobs update
.RE

.RS 2m
$ gcloud beta ai model\-monitoring\-jobs update
.RE


