Kubernetes at its core is a container orchestration system. But simply running containers for their own sake has little purpose, as at the end of the day what really matters are applications.
Among the most interesting and often challenging types of application workloads are machine learning ones, which can often be difficult to deploy and operate. On Dec. 21, the Kubeflow project was officially announced by Google engineers as a new stack to easily deploy and run machine learning workloads.
"The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable," the Kubeflow GitHub project page states. "Our goal is not to recreate other services, but to provide a straightforward way for spinning up best of breed OSS solutions."