Interface UpdateModelDeploymentMonitoringJobRequestOrBuilder (3.52.0)

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public interface UpdateModelDeploymentMonitoringJobRequestOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getModelDeploymentMonitoringJob()

public abstract ModelDeploymentMonitoringJob getModelDeploymentMonitoringJob()

Required. The model monitoring configuration which replaces the resource on the server.

.google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob model_deployment_monitoring_job = 1 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
ModelDeploymentMonitoringJob	The modelDeploymentMonitoringJob.

getModelDeploymentMonitoringJobOrBuilder()

public abstract ModelDeploymentMonitoringJobOrBuilder getModelDeploymentMonitoringJobOrBuilder()

Required. The model monitoring configuration which replaces the resource on the server.

.google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob model_deployment_monitoring_job = 1 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
ModelDeploymentMonitoringJobOrBuilder

getUpdateMask()

public abstract FieldMask getUpdateMask()

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

.google.protobuf.FieldMask update_mask = 2 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
FieldMask	The updateMask.

getUpdateMaskOrBuilder()

public abstract FieldMaskOrBuilder getUpdateMaskOrBuilder()

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

.google.protobuf.FieldMask update_mask = 2 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
FieldMaskOrBuilder

hasModelDeploymentMonitoringJob()

public abstract boolean hasModelDeploymentMonitoringJob()

Required. The model monitoring configuration which replaces the resource on the server.

.google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob model_deployment_monitoring_job = 1 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
boolean	Whether the modelDeploymentMonitoringJob field is set.

hasUpdateMask()

public abstract boolean hasUpdateMask()

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

.google.protobuf.FieldMask update_mask = 2 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
boolean	Whether the updateMask field is set.