Introducing: Machine Learning on Kubernetes advocacy site

I suppose it’s fair to say that in 2017 both Kubernetes went mainstream and Machine Learning is reasonably hyped as well. Also, in the past ~15 months I did notice an increasing interest in and activity around combining Machine Learning and Kubernetes.

Taking the most recent KubeCon + CloudNativeCon as an example, it’s clear that it is happening as we speak: amongst other things, Google announced Kubeflow, a standard Machine Learning stack combining JupyterHub and Tensorflow on Kubernetes:

Kubeflow launch at KubeCon + CloudNativeCon 2017, in Austin, TX.

So I thought it’s time to create a little advocacy site that provides you with curated links to learning material and tooling in this space, meet Kubernetes Machine Learning rocks (KML.rocks):

http://kube-machine-learning.rocks

The idea is to document developments in the ML on Kubernetes space, keep you up to date about events and activities and provide you with reviews and hands-on material.


While admittedly at time of writing the Machine Learning use case on Kubernetes is still a niche, I’m looking forward to see it growing considerably in 2018. If you have any suggestions around material and tools, hit me up on Twitter (my DMs are always open) or leave a comment here!