When developing a new technology, it really helps if you are also a user of that new tech. This has been an approach of Red Hat around artificial intelligence and machine learning — develop openly on one hand, exchanging knowledge across the organization to use the same tools in the other hand to work on interesting business problems. All while keeping a two-way exchange to and from the open source commons.

This is the sort of left-hand/right-hand move that data scientist Oindrilla Chatterjee began using as part of a project she originally started during an internship, then later in a full-time role at Red Hat. Chatterjee and her team are looking at how to do sentiment analysis using machine learning on a dataset consisting of customer and partner surveys regarding a service offering.

The prospect of true machine learning is a tangible goal for data scientists and researchers. It has been long known that the platform on which such ML apps can run have to be fast and hyper efficient so that learning can be that much faster. This is the motivation for Red Hat engineers in the Office of the CTO who are working to optimize such an open source platform: Open Data Hub.

Open Data Hub is built on Red Hat OpenShift Container Platform, Ceph Object Storage, and Apache Kafka/Strimzi integrated into a collection of open source projects to enable a machine-learning-as-a-service platform. That’s a lot of components to be integrated, and to ensure that their contributions to Open Data Hub perform well, Red Hat engineers have taken the step of creating an Internal Data Hub within Red Hat as a proving ground and learning environment.

The concept of artificial intelligence, which seemed so much like science fiction a few decades ago, has made real, practical inroads in producing results that organizations can find useful. What’s making those results happen, though, isn’t esoteric pie-in-the-sky theory: it’s creating statistical models that have been trained to make decisions. And trained a lot.

Artificial intelligence itself is a term that, for now, has had less of a focus than the more results-oriented machine learning, where a computer system is given input and output data and then is directed to infer the mathematical rules that govern the transformation of that data.

“It’s like pointing a program to look at the solar system and then have it figure out the laws of motion that govern a planetary system,” explained Sanjay Arora.