MLOps at the edge: Running AI models at the edge

What happened

MLOps at the edge: Running AI models at the edge, Red Hat Blog announced. Running AI inference in a data center (a building full of computers that runs online services) means using standardized hardware and abundant memory. This article follows that question through platform selection, hardware accelerator dependency management, shared memory and latency challenges, and the messaging patterns that connect sensors to inference and inference to downstream systems.

The hardware reality: Why edge AI deployment differs from cloud AI When running AI at the "core" (cloud or on-premises data centers), you run on standardized hardware. You select a GPU (a chip built for many calculations at once, used for graphics and AI) instance type, the Compute Unified Device Architecture (CUDA) version is known, and memory is measured in tens of gigabytes.

Edge AI fleets are the opposite: heterogeneous by nature, resource-constrained, and in some cases serving operational workloads that compete with AI inference for the same hardware resources. An edge AI inference process could be sharing memory and CPU (the general-purpose processor at the centre of a computer) with the applications' communication stack, the telemetry agent, and the watchdog service.