KServe explainer

The TrustyAI KServe Explainer is a component that provides explanations for predictions made by using the built-in KServe explainer support ^[1]. It supports LIME and SHAP explanation methods, configurable directly within KServe InferenceServices.

Features

Explainability: Integrated support for LIME and SHAP explanation methods to interpret model predictions via the :explain endpoint.

Deployment on KServe

The TrustyAI explainer can be added to KServe InferenceServices and can be configured to use either LIME or SHAP explanation methods by modifying the YAML configuration.

When deployed, KServe manages the routing of requests to the appropriate container. Calls to /v1/models/model:predict will be sent to the predictor container, while calls to /v1/models/model:explain will be sent to the explainer container. The payloads for both endpoints are the same, but the :predict endpoint returns the model’s prediction, while the :explain endpoint returns an explanation of the prediction.

LIME Explainer

By default, the TrustyAI KServe explainer uses the LIME explainer. You can deploy the explainer by specifying the appropriate container image and any necessary configuration in the InferenceService YAML.

SHAP Explainer

To use the SHAP explainer, you can deploy the explainer by specifying it as an environment variable in the InferenceService YAML configuration.

Interacting with the Explainer

You can interact with the explainer using the :explain endpoint. By sending a JSON payload containing the necessary input data, you can retrieve an explanation for the model’s prediction. The response structure includes the saliencies of each feature contributing to the prediction.

A full tutorial on how to deploy the TrustyAI KServe explainer is available at Saliency Explanations with KServe.

1. Documentation available at KServe explainers section.