Cloud AI Platform v1 API - Class ModelDeploymentMonitoringJob (2.8.0)

Reference documentation and code samples for the Cloud AI Platform v1 API class ModelDeploymentMonitoringJob.

Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

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][google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.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. The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum:

1. Training data logging predict request/response
2. Serving data logging predict request/response

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.

Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.

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

EndpointName-typed view over the Endpoint resource name property.

Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

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.

Output only. Latest triggered monitoring pipeline metadata.

Required. Sample Strategy for logging.

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.

ModelDeploymentMonitoringJobName-typed view over the Name resource name property.

Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.

Required. Schedule config for running the monitoring job.

Alert config for model monitoring.

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.

Sample Predict instance, same format as [PredictRequest.instances][google.cloud.aiplatform.v1.PredictRequest.instances], this can be set as a replacement of [ModelDeploymentMonitoringJob.predict_instance_schema_uri][google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.predict_instance_schema_uri]. If not set, we will generate predict schema from collected predict requests.

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'.

Stats anomalies base folder path.

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