Google Cloud Ai Platform V1 Client - Class UpdateModelDeploymentMonitoringJobRequest (1.35.0)

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