As the digital transformation accelerates across enterprises, many are looking for artificial intelligence and machine learning to take greater advantage of their data.

Regardless of industry, everyone is finding a use for AI/ML. So how can Kubernetes help companies with complex data better navigate these technologies?

“AI/ML really wasn’t a core topic where [customers] were looking to use a Kubernetes platform to address those types of workloads, but in the last couple of years that’s really skyrocketed,” said Steven Huels (pictured), senior director of cloud services at Red Hat Inc. “We’re seeing a lot of interest from existing customers.”

Huels spoke with Lisa Martin and David Nicholson, co-hosts of theCUBE, SiliconANGLE Media’s livestreaming studio, during KubeCon + CloudNativeCon NA. They discussed how a Kubernetes platform can be used to help companies with AI/ML. (* Disclosure below.)

Red Hat customers look to take Kubernetes benefits to the limit

Most enterprises have a great handle on experimentation with data scientists and model developing, but many need help to put those models into production MLOps, according to Huels.

“So how do I take what’s been built on somebody’s machine and put that into production in a repeatable way?” Huels asked. “And then once it’s in production, how do I monitor it? What am I looking for as triggers to indicate that I need to retrain? And how do I iterate on this? [It’s] applying what would really be traditional DevOps software development lifecycle methodologies to ML and AI.”

Red Hat’s OpenShift is an enterprise-grade Kubernetes platform. The connection to AI/ML stems from organizations that are looking to take advantage of all the benefits they can get from a Kubernetes platform that they’ve been applying to their traditional software development over the years.

“The agility, the ability to scale up on demand, the ability to have shared resources to make specialized hardware available to individual communities, they want to start applying … those foundational elements to their AI/Ml practices,” Huels stated.

A lot of data science work was traditionally done with high-powered model machines and systems, but those weren’t necessarily shared across development communities. Connecting something that was built by a data scientist to something that a software developer was going to put in production was challenging, Huels added.

“There wasn’t a lot of repeatability in there, there wasn’t a lot of scalability, there wasn’t a lot of audit ability — and these are all things we know we need when we’re talking about AI/ML,” he said. “And so … the connection there is really around taking advantage of what has proven itself in Kubernetes to be a very effective development model and applying that to AI/Ml and getting the benefits in being able to put these things into production.”

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of KubeCon + CloudNativeCon NA. (* Disclosure: Red Hat Inc. sponsored this segment of theCUBE. Neither Red Hat nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: Steven Huels