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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_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 |
MutableSequence[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 |
MutableSequence[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 |
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_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 .
|
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,
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 |
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.
Methods
ModelDeploymentMonitoringJob
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.