Class ModelDeploymentMonitoringJob (1.24.0)

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ModelDeploymentMonitoringJob(
    mapping=None, *, ignore_unknown_fields=False, **kwargs
)

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.

Attributes
Name	Description
name	str Output only. Resource name of a ModelDeploymentMonitoringJob.
display_name	str 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.
endpoint	str Required. Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
state	google.cloud.aiplatform_v1beta1.types.JobState 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'.
schedule_state	google.cloud.aiplatform_v1beta1.types.ModelDeploymentMonitoringJob.MonitoringScheduleState Output only. Schedule state when the monitoring job is in Running state.
latest_monitoring_pipeline_metadata	google.cloud.aiplatform_v1beta1.types.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata Output only. Latest triggered monitoring pipeline metadata.
model_deployment_monitoring_objective_configs	MutableSequence[google.cloud.aiplatform_v1beta1.types.ModelDeploymentMonitoringObjectiveConfig] Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
model_deployment_monitoring_schedule_config	google.cloud.aiplatform_v1beta1.types.ModelDeploymentMonitoringScheduleConfig Required. Schedule config for running the monitoring job.
logging_sampling_strategy	google.cloud.aiplatform_v1beta1.types.SamplingStrategy Required. Sample Strategy for logging.
model_monitoring_alert_config	google.cloud.aiplatform_v1beta1.types.ModelMonitoringAlertConfig Alert config for model monitoring.
predict_instance_schema_uri	str 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	google.protobuf.struct_pb2.Value 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.
analysis_instance_schema_uri	str 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.
bigquery_tables	MutableSequence[google.cloud.aiplatform_v1beta1.types.ModelDeploymentMonitoringBigQueryTable] 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
log_ttl	google.protobuf.duration_pb2.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.
labels	MutableMapping[str, str] 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.
create_time	google.protobuf.timestamp_pb2.Timestamp Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
update_time	google.protobuf.timestamp_pb2.Timestamp Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
next_schedule_time	google.protobuf.timestamp_pb2.Timestamp Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.
stats_anomalies_base_directory	google.cloud.aiplatform_v1beta1.types.GcsDestination Stats anomalies base folder path.
encryption_spec	google.cloud.aiplatform_v1beta1.types.EncryptionSpec 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.
enable_monitoring_pipeline_logs	bool 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
error	google.rpc.status_pb2.Status Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

Inheritance

builtins.object > proto.message.Message > ModelDeploymentMonitoringJob

Classes

LabelsEntry

LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

The abstract base class for a message.

Parameters
Name	Description
kwargs	dict Keys and values corresponding to the fields of the message.
mapping	Union[dict, .Message] A dictionary or message to be used to determine the values for this message.
ignore_unknown_fields	Optional(bool) If True, do not raise errors for unknown fields. Only applied if mapping is a mapping type or there are keyword parameters.

LatestMonitoringPipelineMetadata

LatestMonitoringPipelineMetadata(
    mapping=None, *, ignore_unknown_fields=False, **kwargs
)

All metadata of most recent monitoring pipelines.

MonitoringScheduleState

MonitoringScheduleState(value)

The state to Specify the monitoring pipeline.

Values: MONITORING_SCHEDULE_STATE_UNSPECIFIED (0): Unspecified state. PENDING (1): The pipeline is picked up and wait to run. OFFLINE (2): The pipeline is offline and will be scheduled for next run. RUNNING (3): The pipeline is running.