Virtual handout for my Red Hat Summit talk

Some materials related to my quick overview of machine learning techniques for enterprise developers at Red Hat Summit 2018.

May 8, 2018

It’s an honor to present at Red Hat Summit again this year! I’m giving a brief introduction to machine learning concepts for developers. Of course, one can’t do justice to such a broad topic in a forty-minute session, but I have some materials for people who’d like to experiment with some fundamental ML techniques on their own time.

These materials are all presented as Jupyter notebooks, which combine code, narrative explanations, and output. These notebooks mean that you can inspect code, run it, change it, and experiment with it. The main thing to know about Jupyter is that notebooks are made up of cells, and pressing shift+enter will run the cell you’re currently on and move to the next one. If you get stuck, you can go up to the “Kernel” menu, and select “Restart and clear output.”

First up, this notebook can be run directly in your browser through the service – it presents an introduction to the scalable analytic techniques I mentioned in the beginning of the session.

If you’d like to dive deeper into specific machine learning techniques, you’ll need to fire up OpenShift:

When you visit the route for the Jupyter pod, you’ll need to log in. The password is developer. After you log in, you’ll be presented a with a list of notebook files. Here’s what each of them contain:

Finally, be sure to visit to see examples of intelligent applications on OpenShift and to learn how to enable Apache Kafka on OpenShift.

You’re at the beginning of a really exciting journey! I hope these resources are helpful as you get started.