Kubeflow, Google’s solution for deploying machine learning stacks on Kubernetes, is now available as an official 1.0 release.
Kubeflow was built to address two major issues with machine learning projects: the need for integrated, end-to-end workflows, and the need to make deploments of machine learning systems simple, manageable, and scalable. Kubeflow allows data scientists to build machine learning workflows on Kubernetes and to deploy, manage, and scale machine learning models in production without learning the intricacies of Kubernetes or its components.
Kubeflow is designed to manage every phase of a machine learning project: writing the code, building the containers, allocating the Kubernetes resources to run them, training the models, and serving predictions from those models. The Kubeflow 1.0 release provides tools, such as Jupyter notebooks for working with data experiments and a web-based dashboard UI for general oversight, to help with each phase.