Red Hat Enlists Lockheed Martin to Bring AI to K8s Edge

At the KubeCon + CloudNativeCon North America conference, Red Hat and Lockheed Martin today announced a collaborative effort to build and deploy AI-infused Kubernetes applications on edge computing platforms.

At the core of this initiative is a Red Hat Device Edge platform that incorporates MicroShift, a lightweight Kubernetes orchestration platform based on the Red Hat OpenShift platform, and an edition of Red Hat Enterprise Linux (RHEL) operating system optimized for Kubernetes. In addition, Lockheed Martin is contributing to the open source MicroShift project.

Nick Barcet, senior director for strategy within the CTO office of Red Hat, says the goal is to provide full life cycle management capabilities to streamline the management of both Kubernetes clusters and the underlying operating systems they are deployed on, he says.

Lockheed Martin is already testing AI applications involving instances of Red Hat Device Edge deployed in drones that are being used to detect forest fires faster in Europe, Barcet added.

Red Hat and Lockheed are also working together to equip U.S. military platforms, such as the Stalker unmanned aerial system (UAS), with AI software that was previously too large and complex to be deployed on various types of embedded systems. Lockheed Martin has already demonstrated how Red Hat Device Edge embedded within a Stalker UAS can employ onboard sensors infused with AI to adapt in real-time to a threat environment using computer vision updates that were delivered in-flight to more accurately classify potential targets.

The two companies also revealed they are collaborating with other industry partners to enhance the 5G.MIL wireless networking spec using RAN Intelligent Controller (RIC) functionality based on the Red Hat OpenShift distribution of Kubernetes.

As instances of Kubernetes are deployed across a wide range of edge computing platforms it’s now only a matter of time before operational technology (OT) platforms become more integrated with traditional IT platforms, says Barcet. As more application workloads are distributed to the edge, the need to integrate OT and IT will become much more pressing, he notes.

The primary driver of that growth is an increasing need to process and analyze data closer to the geographic location where that data is being created and consumed. In fact, a large percentage of the Kubernetes applications deployed at the edge will be stateful in that some amount of data will need to be stored locally.

It’s still early days as far as deployments of Kubernetes on the edge are concerned but the number of use cases involving stateful applications infused with machine learning algorithms that tend to process large amounts of data is already increasing. As such, it’s become apparent that Kubernetes is now at the core of a wide range of digital transformation efforts, especially those that require the ability to process and analyze data at the network edge. In fact, it’s now only a matter of time before more data is processed on edge computing platforms than in the cloud, notes Barcet.

In the meantime, the number of applications that will need to be built and deployed at the edge using DevOps best practices appears set to grow at an exponential pace.

Mike Vizard

Mike Vizard is a seasoned IT journalist with over 25 years of experience. He also contributed to IT Business Edge, Channel Insider, Baseline and a variety of other IT titles. Previously, Vizard was the editorial director for Ziff-Davis Enterprise as well as Editor-in-Chief for CRN and InfoWorld.

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