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v1

latestOpenAPI 3.0.0Creative Commons Attribution 3.02026-07-132661,4471.5 MB
projects

Creates a ModelDeploymentMonitoringJob. It will run periodically on a configured interval.

post/v1/{+parent}/modelDeploymentMonitoringJobs

Path parameters

parentstring required

Required. The parent of the ModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}

Request body

state'JOB_STATE_UNSPECIFIED' | 'JOB_STATE_QUEUED' | 'JOB_STATE_PENDING' | 'JOB_STATE_RUNNING' | 'JOB_STATE_SUCCEEDED' | 'JOB_STATE_FAILED' | 'JOB_STATE_CANCELLING' | 'JOB_STATE_CANCELLED' | 'JOB_STATE_PAUSED' | 'JOB_STATE_EXPIRED' | 'JOB_STATE_UPDATING' | 'JOB_STATE_PARTIALLY_SUCCEEDED'

Output only. The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.

createTimestring google-datetime

Output only. Timestamp when this ModelDeploymentMonitoringJob was created.

{"stackTrail":"components:schemas:GoogleCloudAiplatformV1ModelDeploymentMonitoringJob:properties:samplePredictInstance","oasType":"schema","type":"unknown","description":"Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests."}
enableMonitoringPipelineLogsboolean

If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing.

endpointstring

Required. Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

logTtlstring google-duration

The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.

analysisInstanceSchemaUristring

YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.

updateTimestring google-datetime

Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.

labelsobject

The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

namestring

Output only. Resource name of a ModelDeploymentMonitoringJob.

predictInstanceSchemaUristring

YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests.

displayNamestring

Required. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.

satisfiesPziboolean

Output only. Reserved for future use.

satisfiesPzsboolean

Output only. Reserved for future use.

scheduleState'MONITORING_SCHEDULE_STATE_UNSPECIFIED' | 'PENDING' | 'OFFLINE' | 'RUNNING'

Output only. Schedule state when the monitoring job is in Running state.

nextScheduleTimestring google-datetime

Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.

Response

Successful response