kubeflow/kubeflow

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. This document details the steps needed to run the Kubeflow project in any environment in which Kubernetes runs. Our goal is to make scaling machine learning (ML) models and deploying them to production as simple as possible, by letting Kubernetes do what it's great at: Because ML practitioners use a diverse set of tools, one of the key goals is to customize the stack based on user requirements (within reason) and let the system take care of the "boring stuff".