- 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
in regionexample
for endpointus-central1
, run:123
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
, run:123
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
, with training dataset from Google Cloud Storage, run:123
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
, with training dataset from Vertex AI dataset123
, run:456
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.
-
provide the argument
-
provide the argument
--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.
-
provide the argument
--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.
-
provide the argument
--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.
-
provide the argument
--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
.
-
provide the argument
-
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
--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.
--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
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2024-02-06 UTC.
[{
"type": "thumb-down",
"id": "hardToUnderstand",
"label":"Hard to understand"
},{
"type": "thumb-down",
"id": "incorrectInformationOrSampleCode",
"label":"Incorrect information or sample code"
},{
"type": "thumb-down",
"id": "missingTheInformationSamplesINeed",
"label":"Missing the information/samples I need"
},{
"type": "thumb-down",
"id": "otherDown",
"label":"Other"
}]
[{
"type": "thumb-up",
"id": "easyToUnderstand",
"label":"Easy to understand"
},{
"type": "thumb-up",
"id": "solvedMyProblem",
"label":"Solved my problem"
},{
"type": "thumb-up",
"id": "otherUp",
"label":"Other"
}]
{
"lastModified": "Last updated 2024-02-06 UTC.",
"confidential": False
}