Google Cloud Ai Platform V1 Client - Class UpdateModelDeploymentMonitoringJobRequest (1.12.0)

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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

Namespace

Google \ Cloud \ AIPlatform \ V1

Methods

__construct

Constructor.

Parameters
Name	Description
data	array Optional. Data for populating the Message object.
↳ model_deployment_monitoring_job	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.

Returns
Type	Description
ModelDeploymentMonitoringJob|null

hasModelDeploymentMonitoringJob

clearModelDeploymentMonitoringJob

setModelDeploymentMonitoringJob

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

Parameter
Name	Description
var	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

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

Parameter
Name	Description
var	Google\Protobuf\FieldMask

Returns
Type	Description
$this

static::build

Parameters
Name	Description
modelDeploymentMonitoringJob	ModelDeploymentMonitoringJob Required. The model monitoring configuration which replaces the resource on the server.
updateMask	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
Type	Description
UpdateModelDeploymentMonitoringJobRequest