Advanced MLOps & Production
40 MIN READ
The Ray Field Guide: From @ray.remote to a Production GPU Cluster
Every Ray concept an ML engineer is expected to know, in one place: tasks, actors, objects and ownership, scheduling and placement groups, fault tolerance, Ray Data, Train, Tune, Serve, RLlib, compiled graphs and direct GPU transport, KubeRay, observability, and the anti-patterns that waste GPU hours. Mapped onto PyTorch DDP, NCCL and Kubernetes, with self-check questions at the end.