Class ModelDeploymentMonitoringJob (1.17.1)

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

NameDescription
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 be 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_v1.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_v1.types.ModelDeploymentMonitoringJob.MonitoringScheduleState
Output only. Schedule state when the monitoring job is in Running state.
latest_monitoring_pipeline_metadata google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata
Output only. Latest triggered monitoring pipeline metadata.
model_deployment_monitoring_objective_configs Sequence[google.cloud.aiplatform_v1.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_v1.types.ModelDeploymentMonitoringScheduleConfig
Required. Schedule config for running the monitoring job.
logging_sampling_strategy google.cloud.aiplatform_v1.types.SamplingStrategy
Required. Sample Strategy for logging.
model_monitoring_alert_config google.cloud.aiplatform_v1.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 Sequence[google.cloud.aiplatform_v1.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 Mapping[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_v1.types.GcsDestination
Stats anomalies base folder path.
encryption_spec google.cloud.aiplatform_v1.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
NameDescription
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.