Ray / Ray Train / Ray Serve is the distributed-computing framework ML Infrastructure Engineers use to scale model training and serving across clusters of machines without hand-rolling orchestration logic. It's become a default choice at Indian companies running large-scale ML pipelines because it slots cleanly into Kubernetes-based infra, and engineers fluent in it are scarce relative to demand. Most learn it through the official Ray documentation and hands-on cluster projects rather than a formal course.
The skills most often needed alongside Ray / Ray Train / Ray Serve in the same roles — build these together to widen your options.
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