Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::FeaturestoreMonitoringConfig::ThresholdConfig (v0.3.0)

Version 0.3.0

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

The config for Featurestore Monitoring threshold.

Inherits

• Object

Extended By

• Google::Protobuf::MessageExts::ClassMethods

Includes

• Google::Protobuf::MessageExts

Methods

#value

def value() -> ::Float

Returns

• (::Float) —

  Specify a threshold value that can trigger the alert.

  1. For categorical feature, the distribution distance is calculated by L-inifinity norm.
  2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

#value=

def value=(value) -> ::Float

Parameter

• value (::Float) —

  Specify a threshold value that can trigger the alert.

  1. For categorical feature, the distribution distance is calculated by L-inifinity norm.
  2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

Returns

• (::Float) —

  Specify a threshold value that can trigger the alert.

  1. For categorical feature, the distribution distance is calculated by L-inifinity norm.
  2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.