Google Cloud Ai Platform V1 Client - Class UpdateModelDeploymentMonitoringJobRequest (0.10.0)

Version 0.10.0

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Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class UpdateModelDeploymentMonitoringJobRequest.

Request message for JobService.UpdateModelDeploymentMonitoringJob.

Generated from protobuf message google.cloud.aiplatform.v1.UpdateModelDeploymentMonitoringJobRequest

Methods

__construct

Constructor.

Parameters
Name	Description
data	array Optional. Data for populating the Message object.
↳ model_deployment_monitoring_job	Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringJob Required. The model monitoring configuration which replaces the resource on the server.
↳ update_mask	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

getModelDeploymentMonitoringJob

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

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

Returns
Type	Description
Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringJob|null

hasModelDeploymentMonitoringJob

clearModelDeploymentMonitoringJob

setModelDeploymentMonitoringJob

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

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

Parameter
Name	Description
var	Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringJob

Returns
Type	Description
$this

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

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

Returns
Type	Description
Google\Protobuf\FieldMask|null

hasUpdateMask

clearUpdateMask

setUpdateMask

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

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

Parameter
Name	Description
var	Google\Protobuf\FieldMask

Returns
Type	Description
$this