Struct ModelMonitoringObjectiveConfig 

#[non_exhaustive]
pub struct ModelMonitoringObjectiveConfig {
    pub training_dataset: Option<TrainingDataset>,
    pub training_prediction_skew_detection_config: Option<TrainingPredictionSkewDetectionConfig>,
    pub prediction_drift_detection_config: Option<PredictionDriftDetectionConfig>,
    pub explanation_config: Option<ExplanationConfig>,
    /* private fields */
}

Available on crate feature job-service only.

The objective configuration for model monitoring, including the information needed to detect anomalies for one particular model.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

training_dataset: Option<TrainingDataset>

Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.

training_prediction_skew_detection_config: Option<TrainingPredictionSkewDetectionConfig>

The config for skew between training data and prediction data.

prediction_drift_detection_config: Option<PredictionDriftDetectionConfig>

The config for drift of prediction data.

explanation_config: Option<ExplanationConfig>

The config for integrating with Vertex Explainable AI.

Implementations

impl ModelMonitoringObjectiveConfig

pub fn new() -> Self

Creates a new default instance.

pub fn set_training_dataset<T>(self, v: T) -> Self

where T: Into<TrainingDataset>,

Sets the value of training_dataset.

Example

use google_cloud_aiplatform_v1::model::model_monitoring_objective_config::TrainingDataset;
let x = ModelMonitoringObjectiveConfig::new().set_training_dataset(TrainingDataset::default()/* use setters */);

pub fn set_or_clear_training_dataset<T>(self, v: Option<T>) -> Self

where T: Into<TrainingDataset>,

Sets or clears the value of training_dataset.

Example

use google_cloud_aiplatform_v1::model::model_monitoring_objective_config::TrainingDataset;
let x = ModelMonitoringObjectiveConfig::new().set_or_clear_training_dataset(Some(TrainingDataset::default()/* use setters */));
let x = ModelMonitoringObjectiveConfig::new().set_or_clear_training_dataset(None::<TrainingDataset>);

pub fn set_training_prediction_skew_detection_config<T>(self, v: T) -> Self

where T: Into<TrainingPredictionSkewDetectionConfig>,

Sets the value of training_prediction_skew_detection_config.

Example

use google_cloud_aiplatform_v1::model::model_monitoring_objective_config::TrainingPredictionSkewDetectionConfig;
let x = ModelMonitoringObjectiveConfig::new().set_training_prediction_skew_detection_config(TrainingPredictionSkewDetectionConfig::default()/* use setters */);

pub fn set_or_clear_training_prediction_skew_detection_config<T>( self, v: Option<T>, ) -> Self

where T: Into<TrainingPredictionSkewDetectionConfig>,

Sets or clears the value of training_prediction_skew_detection_config.

Example

use google_cloud_aiplatform_v1::model::model_monitoring_objective_config::TrainingPredictionSkewDetectionConfig;
let x = ModelMonitoringObjectiveConfig::new().set_or_clear_training_prediction_skew_detection_config(Some(TrainingPredictionSkewDetectionConfig::default()/* use setters */));
let x = ModelMonitoringObjectiveConfig::new().set_or_clear_training_prediction_skew_detection_config(None::<TrainingPredictionSkewDetectionConfig>);

pub fn set_prediction_drift_detection_config<T>(self, v: T) -> Self

where T: Into<PredictionDriftDetectionConfig>,

Sets the value of prediction_drift_detection_config.

Example

use google_cloud_aiplatform_v1::model::model_monitoring_objective_config::PredictionDriftDetectionConfig;
let x = ModelMonitoringObjectiveConfig::new().set_prediction_drift_detection_config(PredictionDriftDetectionConfig::default()/* use setters */);

pub fn set_or_clear_prediction_drift_detection_config<T>( self, v: Option<T>, ) -> Self

where T: Into<PredictionDriftDetectionConfig>,

Sets or clears the value of prediction_drift_detection_config.

Example

use google_cloud_aiplatform_v1::model::model_monitoring_objective_config::PredictionDriftDetectionConfig;
let x = ModelMonitoringObjectiveConfig::new().set_or_clear_prediction_drift_detection_config(Some(PredictionDriftDetectionConfig::default()/* use setters */));
let x = ModelMonitoringObjectiveConfig::new().set_or_clear_prediction_drift_detection_config(None::<PredictionDriftDetectionConfig>);

pub fn set_explanation_config<T>(self, v: T) -> Self

where T: Into<ExplanationConfig>,

Sets the value of explanation_config.

Example

use google_cloud_aiplatform_v1::model::model_monitoring_objective_config::ExplanationConfig;
let x = ModelMonitoringObjectiveConfig::new().set_explanation_config(ExplanationConfig::default()/* use setters */);

pub fn set_or_clear_explanation_config<T>(self, v: Option<T>) -> Self

where T: Into<ExplanationConfig>,

Sets or clears the value of explanation_config.

Example

use google_cloud_aiplatform_v1::model::model_monitoring_objective_config::ExplanationConfig;
let x = ModelMonitoringObjectiveConfig::new().set_or_clear_explanation_config(Some(ExplanationConfig::default()/* use setters */));
let x = ModelMonitoringObjectiveConfig::new().set_or_clear_explanation_config(None::<ExplanationConfig>);

Trait Implementations

impl Clone for ModelMonitoringObjectiveConfig

fn clone(&self) -> ModelMonitoringObjectiveConfig

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ModelMonitoringObjectiveConfig

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ModelMonitoringObjectiveConfig

fn default() -> ModelMonitoringObjectiveConfig

Returns the “default value” for a type.

impl Message for ModelMonitoringObjectiveConfig

fn typename() -> &'static str

The typename of this message.

impl PartialEq for ModelMonitoringObjectiveConfig

fn eq(&self, other: &ModelMonitoringObjectiveConfig) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl StructuralPartialEq for ModelMonitoringObjectiveConfig