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KServe

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KServe provides a Kubernetes Custom Resource Definition for serving predictive and generative machine learning (ML) models. It aims to solve production model serving use cases by providing high abstraction interfaces for Tensorflow, XGBoost, ScikitLearn, PyTorch, Huggingface Transformer/LLM models using standardized data plane protocols.

It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. It enables a simple, pluggable, and complete story for Production ML Serving including prediction, pre-processing, post-processing and explainability. KServe is being used across various organizations.

For more details, visit the KServe website.

KServe

KFServing has been rebranded to KServe since v0.7.

Why KServe?

Learn More

To learn more about KServe, how to use various supported features, and how to participate in the KServe community, please follow the KServe website documentation. Additionally, we have compiled a list of presentations and demos to dive through various details.

:hammer_and_wrench: Installation

Standalone Installation

Kubeflow Installation

KServe is an important addon component of Kubeflow, please learn more from the Kubeflow KServe documentation. Check out the following guides for running on AWS or on OpenShift Container Platform.

:flight_departure: Create your first InferenceService

:bulb: Roadmap

:blue_book: InferenceService API Reference

:toolbox: Developer Guide

:writing_hand: Contributor Guide

:handshake: Adopters