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This session explains how Ray is engineered to run reliably on clusters exceeding 10,000 nodes despite network flakiness, spot preemptions, hardware failures, and resource contention. It covers fault tolerance, state management, recovery, elasticity, and workload-aware scheduling for large-scale AI workloads.
