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Deploy InferenceService with Alibi Outlier/Drift Detector

In order to trust and reliably act on model predictions, it is crucial to monitor the distribution of the incoming requests via various different type of detectors. KServe integrates Alibi Detect with the following components:

  • Drift detector checks when the distribution of incoming requests is diverging from a reference distribution such as that of the training data.
  • Outlier detector flags single instances which do not follow the training distribution.

The architecture used is shown below and links the payload logging available within KServe with asynchronous processing of those payloads in KNative to detect outliers.

Architetcure

CIFAR10 Outlier Detector

A CIFAR10 Outlier Detector. Run the notebook demo to test.

The notebook requires KNative Eventing >= 0.18.

CIFAR10 Drift Detector

A CIFAR10 Drift Detector. Run the notebook demo to test.

The notebook requires KNative Eventing >= 0.18.