Building ML Products With Kubeflow
ML researchers spend too much time building infrastructure to support their work. Kubeflow aims to solve that by using Kubernetes to build an open, scalable, and extensible platform for ML. Since our …
| Talk Title | Building ML Products With Kubeflow |
| Speakers | Stephan Fabel (Product Manager, Canonical), Jeremy Lewi (Senior Software Developer, Google) |
| Conference | KubeCon + CloudNativeCon Europe |
| Conf Tag | |
| Location | Copenhagen, Denmark |
| Date | Apr 30-May 4, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
ML researchers spend too much time building infrastructure to support their work. Kubeflow aims to solve that by using Kubernetes to build an open, scalable, and extensible platform for ML. Since our launch at Kubecon in December, Kubeflow has grown to a substantial Github community with over 2200 stars and contributors from companies across the Kubernetes ecosystem, including Red Hat, Canonical, Weaveworks, CoreOS, CaiCloud, Alibaba, NVidia and many more. In this talk, we discuss how Kubeflow enables machine learning workflows that are easy enough for anyone to deploy, and run anywhere Kubernetes runs. We will talk about our experience building Kubeflow by leveraging Kubernetes technologies like CRDs and ksonnet to build an extensible, community driven ecosystem. Finally, we will talk about how we are trying to grow the community around Kubeflow to continue evolving the platform.
