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):

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.