Eco-Friendly ML: How the Kubeflow Ecosystem Bootstrapped Itself
How do you bootstrap an open source project that aims to provide stability, composability, and portability for machine learning? You use Kubernetes and its rich ecosystem to implement the pieces of in …
| Talk Title | Eco-Friendly ML: How the Kubeflow Ecosystem Bootstrapped Itself |
| Speakers | Peter MacKinnon (Principal Software Engineer, Red Hat) |
| Conference | KubeCon + CloudNativeCon North America |
| Conf Tag | |
| Location | Seattle, WA, USA |
| Date | Dec 9-14, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
How do you bootstrap an open source project that aims to provide stability, composability, and portability for machine learning? You use Kubernetes and its rich ecosystem to implement the pieces of infrastructure that you need to deliver a comprehensive ML platform for data scientists and DevOps engineers alike. This talk will explore the various integrations that have enabled Kubeflow to quickly emerge as the de-facto machine learning toolkit for Kubernetes. We’ll look in detail at not only how Kubeflow leverages Ambassador, Argo, Ksonnet, and JupyterHub, but also examine integration with complementary projects such as Pachyderm and SeldonIO. You will leave this talk with a better understanding and inspiration of how a particular project can rapidly achieve its potential by working with other projects, and that those inter-project collaborations enrich the entire Kubernetes community.
