For platform engineering teams, running Artificial Intelligence workloads on Kubernetes has long been an exercise in "franken-stacking." A production-ready AI environment rarely consists of a single tool; instead, it is a complex, fragile assembly of queueing systems, distributed runtimes, specialized GPU health checks, and a labyrinth of bespoke submission scripts that hold the entire ecosystem […]

