Kubeflow KFServing 0.6 is Out!
Congratulations to the Kubeflow community and especially those in the KFServing working group on releasing KFServing 0.6 last week. If you are fairly new to Kubeflow or how development of the project is organized, here’s a quick primer.
The Kubeflow project is comprised of multiple technologies that when combined deliver a machine learning platform. For the sake of manageability, the Kubeflow project has been broken down into seven working groups with associated GitHub repositories. This makes it easier to develop, document and release specific building blocks of functionality.
KFServing (currently in Beta) enables serverless inferencing on Kubernetes and delivers high performance and abstraction interfaces for machine learning frameworks like TensorFlow, XGBoost, scikit-learn, PyTorch, and ONNX.
This new release included 18 new features, 13 fixes, 1 change and 12 Docs/developer experience fixes. Here’s some of the highlights:
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