From a Single Python Process to an AI Cluster: How Ray Hides the Cost of Scaling
When machine learning workloads move beyond one machine, the hard part is not simply adding more CPUs. It is redesigning the boundaries between tasks, state, data, and services. Ray provides a Python native distributed execution model built around tasks, actors, and objects, then adds AI libraries such as Data, Train, Tune, RLlib, and Serve. This article uses a verifiable getting started example to explain where Ray fits and when it should not be introduced too early.