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Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::UpdateModelDeploymentMonitoringJobRequest.
Request message for JobService.UpdateModelDeploymentMonitoringJob.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#model_deployment_monitoring_job
def model_deployment_monitoring_job() -> ::Google::Cloud::AIPlatform::V1::ModelDeploymentMonitoringJob- (::Google::Cloud::AIPlatform::V1::ModelDeploymentMonitoringJob) — Required. The model monitoring configuration which replaces the resource on the server.
#model_deployment_monitoring_job=
def model_deployment_monitoring_job=(value) -> ::Google::Cloud::AIPlatform::V1::ModelDeploymentMonitoringJob- value (::Google::Cloud::AIPlatform::V1::ModelDeploymentMonitoringJob) — Required. The model monitoring configuration which replaces the resource on the server.
- (::Google::Cloud::AIPlatform::V1::ModelDeploymentMonitoringJob) — Required. The model monitoring configuration which replaces the resource on the server.
#update_mask
def update_mask() -> ::Google::Protobuf::FieldMask-
(::Google::Protobuf::FieldMask) —
Required. The update mask is used to specify the fields to be overwritten in the ModelDeploymentMonitoringJob resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to
*to override all fields. For the objective config, the user can either provide the update mask for model_deployment_monitoring_objective_configs or any combination of its nested fields, such as: model_deployment_monitoring_objective_configs.objective_config.training_dataset.Updatable fields:
display_namemodel_deployment_monitoring_schedule_configmodel_monitoring_alert_configlogging_sampling_strategylabelslog_ttlenable_monitoring_pipeline_logs. andmodel_deployment_monitoring_objective_configs. ormodel_deployment_monitoring_objective_configs.objective_config.training_datasetmodel_deployment_monitoring_objective_configs.objective_config.training_prediction_skew_detection_configmodel_deployment_monitoring_objective_configs.objective_config.prediction_drift_detection_config
#update_mask=
def update_mask=(value) -> ::Google::Protobuf::FieldMask-
value (::Google::Protobuf::FieldMask) —
Required. The update mask is used to specify the fields to be overwritten in the ModelDeploymentMonitoringJob resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to
*to override all fields. For the objective config, the user can either provide the update mask for model_deployment_monitoring_objective_configs or any combination of its nested fields, such as: model_deployment_monitoring_objective_configs.objective_config.training_dataset.Updatable fields:
display_namemodel_deployment_monitoring_schedule_configmodel_monitoring_alert_configlogging_sampling_strategylabelslog_ttlenable_monitoring_pipeline_logs. andmodel_deployment_monitoring_objective_configs. ormodel_deployment_monitoring_objective_configs.objective_config.training_datasetmodel_deployment_monitoring_objective_configs.objective_config.training_prediction_skew_detection_configmodel_deployment_monitoring_objective_configs.objective_config.prediction_drift_detection_config
-
(::Google::Protobuf::FieldMask) —
Required. The update mask is used to specify the fields to be overwritten in the ModelDeploymentMonitoringJob resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to
*to override all fields. For the objective config, the user can either provide the update mask for model_deployment_monitoring_objective_configs or any combination of its nested fields, such as: model_deployment_monitoring_objective_configs.objective_config.training_dataset.Updatable fields:
display_namemodel_deployment_monitoring_schedule_configmodel_monitoring_alert_configlogging_sampling_strategylabelslog_ttlenable_monitoring_pipeline_logs. andmodel_deployment_monitoring_objective_configs. ormodel_deployment_monitoring_objective_configs.objective_config.training_datasetmodel_deployment_monitoring_objective_configs.objective_config.training_prediction_skew_detection_configmodel_deployment_monitoring_objective_configs.objective_config.prediction_drift_detection_config