Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::ModelMonitoringObjectiveConfig::PredictionDriftDetectionConfig (v0.12.0)

Version 0.12.0

• 0.55.0 (latest)
• 0.54.0
• 0.53.0
• 0.52.0
• 0.51.0
• 0.50.0
• 0.49.0
• 0.48.0
• 0.47.0
• 0.46.0
• 0.45.0
• 0.44.0
• 0.43.0
• 0.42.0
• 0.41.0
• 0.40.0
• 0.39.0
• 0.38.0
• 0.37.0
• 0.36.0
• 0.35.0
• 0.34.0
• 0.33.0
• 0.32.0
• 0.31.0
• 0.30.0
• 0.29.0
• 0.28.0
• 0.27.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.0
• 0.19.0
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.0
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.1
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0
• 0.1.0

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::ModelMonitoringObjectiveConfig::PredictionDriftDetectionConfig.

The config for Prediction data drift detection.

Inherits

• Object

Extended By

• Google::Protobuf::MessageExts::ClassMethods

Includes

• Google::Protobuf::MessageExts

Methods

#attribution_score_drift_thresholds

def attribution_score_drift_thresholds() -> ::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}

Returns

• (::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}) — Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.

#attribution_score_drift_thresholds=

def attribution_score_drift_thresholds=(value) -> ::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}

Parameter

• value (::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}) — Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.

Returns

• (::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}) — Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.

#default_drift_threshold

def default_drift_threshold() -> ::Google::Cloud::AIPlatform::V1::ThresholdConfig

Returns

• (::Google::Cloud::AIPlatform::V1::ThresholdConfig) — Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.

#default_drift_threshold=

def default_drift_threshold=(value) -> ::Google::Cloud::AIPlatform::V1::ThresholdConfig

Parameter

• value (::Google::Cloud::AIPlatform::V1::ThresholdConfig) — Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.

Returns

• (::Google::Cloud::AIPlatform::V1::ThresholdConfig) — Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.

#drift_thresholds

def drift_thresholds() -> ::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}

Returns

• (::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}) — Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

#drift_thresholds=

def drift_thresholds=(value) -> ::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}

Parameter

• value (::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}) — Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

Returns

• (::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}) — Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.