Kubeflow is a portable and scalable ML/AI Stack built for Kubernetes introduced by Google in late, 2017, as Kubernetes has quickly become the hybrid solution for deploying complicated workloads anywhere.
While Kubernetes was created as an open source container orchestration tool, it has since gained huge popularity; as many adjunct programs develop around it, now Google has announced the release of version 0.1 of the Kubeflow open source tool, designed to bring machine learning to Kubernetes containers.
Kubeflow started with just stateless services, but now customers have begun to move complex workloads to the platform, taking advantage of rich APIs, reliability and performance provided by Kubernetes.
Google in response to the huge demands, moved Kubernetes into the Cloud Native Computing Foundation, as it continues to be actively involved, and Kubeflow is the result of its effort in expanding the project.
The project begins another milestone, as it brings a new level of stability, with a slew of new features that the community has been asking for, which include an interactive training on machine learning jobs and Tensorflow training and hosting support, also Jupyter Hub for collaborative and among others.
The Kubelow community is currently numbering over 70 contributors, and about 20 contributing organizations along with over 700 commits in 15 repositories worldwide.