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Update README.md (kubeflow#11)
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ellistarn authored and k8s-ci-robot committed Apr 9, 2019
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# kfserving
# KFServing
KF Serving provides a Custom Resource Definition for serving ML Models on arbitrary frameworks. It aims to solve 80% of model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and Custom containers.

A KFService encapsulates the complexity of autoscaling, networking, health checking, server configuration, and more, to provide customers with a simple and seamless experience when deploying models.
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