Class: Google::Cloud::AIPlatform::V1::UpdateModelDeploymentMonitoringJobRequest

Inherits:
Object
  • Object
show all
Extended by:
Protobuf::MessageExts::ClassMethods
Includes:
Protobuf::MessageExts
Defined in:
proto_docs/google/cloud/aiplatform/v1/job_service.rb

Overview

Instance Attribute Summary collapse

Instance Attribute Details

#model_deployment_monitoring_job::Google::Cloud::AIPlatform::V1::ModelDeploymentMonitoringJob

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

Returns:



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# File 'proto_docs/google/cloud/aiplatform/v1/job_service.rb', line 840

class UpdateModelDeploymentMonitoringJobRequest
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#update_mask::Google::Protobuf::FieldMask

Returns 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


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# File 'proto_docs/google/cloud/aiplatform/v1/job_service.rb', line 840

class UpdateModelDeploymentMonitoringJobRequest
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end