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KServe: The next generation of KFServing

· 4 min read
Dan Sun
Dan Sun
Co-Founder, KServe
Animesh Singh
Animesh Singh
KServe Contributor

Published on September 27, 2021

We are excited to announce the next chapter for KFServing. In coordination with the Kubeflow Project Steering Group, the KFServing GitHub repository has now been transferred to an independent KServe GitHub organization under the stewardship of the Kubeflow Serving Working Group leads.

The project has been rebranded from KFServing to KServe, and we are planning to graduate the project from Kubeflow Project later this year.

KFServing to KServe Transition

🎯 Project Background​

Developed collaboratively by Google, IBM, Bloomberg, NVIDIA, and Seldon in 2019, KFServing was published as open source in early 2019. The project sets out to provide the following features:

  • A simple, yet powerful, Kubernetes Custom Resource for deploying machine learning (ML) models on production across ML frameworks.
  • Provide performant, standardized inference protocol.
  • Serverless inference according to live traffic patterns, supporting "Scale-to-zero" on both CPUs and GPUs.
  • Complete story for production ML Model Serving including prediction, pre/post-processing, explainability, and monitoring.
  • Support for deploying thousands of models at scale and inference graph capability for multiple models.

KFServing was created to address the challenges of deploying and monitoring machine learning models on production for organizations. After publishing the open source project, we've seen an explosion in demand for the software, leading to strong adoption and community growth. The scope of the project has since increased, and we have developed multiple components along the way, including our own growing body of documentation that needs its own website and independent GitHub organization.

🚀 What's Next​

Over the coming weeks, we will be releasing KServe 0.7 outside of the Kubeflow Project and will provide more details on how to migrate from KFServing to KServe with minimal disruptions. KFServing 0.5.x/0.6.x releases are still supported in next six months after KServe 0.7 release. We are also working on integrating core Kubeflow APIs and standards for the conformance program.

For contributors, please follow the KServe developer and doc contribution guide to make code or doc contributions. We are excited to work with you to make KServe better and promote its adoption by more and more users!

KServe Logo

🔗 KServe Key Links​

🙏 Contributor Acknowledgement​

We'd like to thank all the KServe contributors for this transition work!

Individual Contributors:

Core Contributors: The KServe maintainers and Kubeflow Serving Working Group leads

Community: Everyone who supported this important transition and helped establish KServe as an independent project

🤝 Join the Community​

Welcome to KServe!

The KServe team is committed to making machine learning model serving simple, scalable, and standardized. Thank you for being part of this exciting transition!