The Kubeflow project was announced back in December 2017 and has since become a very popular machine learning platform with both data scientists and MLOps engineers. If you are new to the Kubeflow ecosystem and community, here's a quick rundown. Kubeflow is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. In a nutshell, Kubeflow is the machine learning toolkit for Kubernetes. As such, anywhere you are running Kubernetes, you should also be able to run Kubeflow.

Kubeflow is an open-source project, dedicated to making deployments of ML projects simpler, portable and scalable. 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. But how do we get started? Do we need a Kubernetes cluster?

A Data Scientist spends most of his time inside a Jupyter Notebook exploring the data and drafting ideas. Usually, when we try to version our work, we end up with a bunch of duplicated ipynb files, assuming different naming schemes. Can we have something that automatically snapshots our work, before and after every step in an ML pipeline? Moreover, can we get started using it without a ton of configuration needed? Just open a Notebook, do our thing and be sure that everything else will take care of itself.