We're about to wrap up the first month, so a good point in time to review what happened so far in 2018. Exciting times with a lot of interest in the Machine Learning space; if there's anything I might have overlooked in the Kubernetes Machine Learning space, you let me know please?
TensorFlow Dev Summit
Machine Learning on OpenShift
There is a SIG for Machine Learning on OpenShift in the context of the OpenShift Commons. Meet you there!
Making Machine Learning on Kubernetes Portable and Observable
In this DZone step-by-step tutorial Tamao Nakahara shows how to set up Kubeflow using Weave Cloud on the Google Cloud Platform.
Introducing Seldon Core — Machine Learning Deployment for Kubernetes
Alex Housley, the CEO of Seldon, has a detailed blog pos on the opensourcing of their Seldon platform. You can install it via Helm and it comes with a custom resource definition, allowing for a seamless experience. I'm super interested to learn more about it
Tooling & Tutorials
- chiphuyen/stanford-tensorflow-tutorials … code examples for the Stanford's course: TensorFlow for Deep Learning Research
- deepinsight/mxnet-operator … tools for ML/MXNet on Kubernetes (not sure if that's a dead end but curious to learn).
Some ten days ago the KubeFlow community held its first on-line meeting with over 60 participants world-wide, in two batches. You may wish to peruse the meeting notes to learn more what was discussed and decided.
Header photo by Jaxon Stevens / Unsplash