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Model serving using KFServing

KFServing enables serverless inferencing on Kubernetes and provides performant, high abstraction interfaces for common machine learning (ML) frameworks like TensorFlow, XGBoost, scikit-learn, PyTorch, and ONNX to solve production model serving use cases.

You can use KFServing to do the following:

  • Provide a Kubernetes Custom Resource Definition for serving ML models on arbitrary frameworks.

  • Encapsulate 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.

  • Enable a simple, pluggable, and complete story for your production ML inference server by providing prediction, pre-processing, post-processing and explainability out of the box.

Our strong community contributions help KFServing to grow. We have a Technical Steering Committee driven by Google, IBM, Microsoft, Seldon, and Bloomberg. Browse the KFServing GitHub repo to give us feedback!

Install with Kubeflow

KFServing works with Kubeflow 0.7. Kustomize installation files are located in the manifests repo.



Sample notebooks

We frequently add examples to our GitHub repo.

Learn more


Knative Serving (v0.8.0 +) and Istio (v1.1.7+) should be available on your Kubernetes cluster.

Read more about installing Knative on a Kubernetes cluster.

KFServing installation using kubectl

The following commands install KFServing 0.2.2, using a yaml file in GitHub repo. See here for other available releases. Alternatively, you can clone the GitHub repo and run kubectl on top of it.

kubectl apply -f ${CONFIG_URI}


  1. Install the SDK.

    pip install kfserving
  2. Follow the example to use the KFServing SDK to create, patch, roll out, and delete a KFServing instance.