Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::UpdateModelDeploymentMonitoringJobRequest (v0.3.0)

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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
Returns

#model_deployment_monitoring_job=

def model_deployment_monitoring_job=(value) -> ::Google::Cloud::AIPlatform::V1::ModelDeploymentMonitoringJob
Parameter
Returns

#update_mask

def update_mask() -> ::Google::Protobuf::FieldMask
Returns
  • (::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_name
    • model_deployment_monitoring_schedule_config
    • model_monitoring_alert_config
    • logging_sampling_strategy
    • labels
    • log_ttl
    • enable_monitoring_pipeline_logs . and
    • model_deployment_monitoring_objective_configs . or
    • model_deployment_monitoring_objective_configs.objective_config.training_dataset
    • model_deployment_monitoring_objective_configs.objective_config.training_prediction_skew_detection_config
    • model_deployment_monitoring_objective_configs.objective_config.prediction_drift_detection_config

#update_mask=

def update_mask=(value) -> ::Google::Protobuf::FieldMask
Parameter
  • 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_name
    • model_deployment_monitoring_schedule_config
    • model_monitoring_alert_config
    • logging_sampling_strategy
    • labels
    • log_ttl
    • enable_monitoring_pipeline_logs . and
    • model_deployment_monitoring_objective_configs . or
    • model_deployment_monitoring_objective_configs.objective_config.training_dataset
    • model_deployment_monitoring_objective_configs.objective_config.training_prediction_skew_detection_config
    • model_deployment_monitoring_objective_configs.objective_config.prediction_drift_detection_config
Returns
  • (::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_name
    • model_deployment_monitoring_schedule_config
    • model_monitoring_alert_config
    • logging_sampling_strategy
    • labels
    • log_ttl
    • enable_monitoring_pipeline_logs . and
    • model_deployment_monitoring_objective_configs . or
    • model_deployment_monitoring_objective_configs.objective_config.training_dataset
    • model_deployment_monitoring_objective_configs.objective_config.training_prediction_skew_detection_config
    • model_deployment_monitoring_objective_configs.objective_config.prediction_drift_detection_config