POST /v1/projects/{projectsId}/locations/{locationsId}/modelDeploymentMonitoringJobs
Creates a ModelDeploymentMonitoringJob. It will run periodically on a configured interval.
Request
- Base URLhttps://aiplatform.googleapis.com
- AuthOAuth 2 and OAuth 2Implicit: https://accounts.google.com/o/oauth2/authScopes: https://www.googleapis.com/auth/cloud-platformAuthorization code: https://accounts.google.com/o/oauth2/authToken URL: https://accounts.google.com/o/oauth2/token scopes: https://www.googleapis.com/auth/cloud-platform
Path parameters
Request body
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.
Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
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.
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.
Required. Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
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.
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.
Output only. Resource name of a ModelDeploymentMonitoringJob.
Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.
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.
Output only. Reserved for future use.
Output only. Reserved for future use.
Output only. Schedule state when the monitoring job is in Running state.
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'.
Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
Response
Successful response
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.
Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
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.
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.
Required. Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
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.
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.
Output only. Resource name of a ModelDeploymentMonitoringJob.
Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.
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.
Output only. Reserved for future use.
Output only. Reserved for future use.
Output only. Schedule state when the monitoring job is in Running state.
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'.
Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
Changes
No recorded changes to this operation across all 8 revisions of this API.