Many companies wonder how to properly apply AI/ML tools to their work and line of business applications. One good way to find such an application’s use is to take a gander at what the rest of the business world is doing with machine learning, and attempt to find a location inside your organization where it could be applied.
As an example, much has been made of the Netflix recommendation engine, which was the product of a public $1 million algorithm challenge. The resulting feature helps millions of Netflix subscribers navigate an enormous library of shows and movies. That increases user enjoyment of the platform, and helps the service to become more useful to the use.
How can this recommendation engine model be applied to your business? How is it applied to our own business, here at Red Hat? We have some top minds working on these machine learning applications, and in the process we’ve discovered some interesting things about the open source projects that fuel the Red Hat OpenShift Platform.
In this tutorial, we will learn how open data hub speeds AI Development and Fixed a Kubernetes Bottleneck and learn how machine learning applications intersect with the open source projects that fuel the Red Hat OpenShift Platform.