MLOps at the Edge: Running AI Models at the Edge
Red Hat, Thursday, October 8th, 2026
Part 2 of Red Hat's edge MLOps series covers platforms, accelerators, memory and messaging for edge AI inference.
The second part of Red Hat's MLOps at the edge series follows a signed, optimized OCI model artifact out to edge devices.
It explains why edge AI differs from data center inference, where hardware is standardized and memory plentiful, since edge fleets span heterogeneous, constrained platforms.
The article covers platform selection, hardware accelerator dependency management, shared memory and latency challenges, as well as messaging patterns connecting sensors to inference and inference to downstream systems.