Overview of What S New In Ray And Anyscale Kuberay Connect Native Rdma And More Ray Summit 2026
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Platform Orchestration: Anyscale + Ray on Kubernetes | Anyscale | Ray Summit 2026
Ray and MAI-Thinking-1 | Microsoft AI | Ray Summit 2026
Production-Ready Distributed Inference with Ray Serve | Anyscale | Ray Summit 2026
Ray History Server: Observability for Ephemeral K8s Clusters | KubeRay | Ray Summit 2026
Scaling Frontier AI with Ray on Kubernetes | KubeRay | Ray Summit 2026
Ray Train on Anyscale: Observability and Reliability at Scale | Anyscale | Ray Summit 2026
Building a Virtual Biology Platform on Ray | Recursion | Ray Summit 2026
AMD and vLLM: What's New | AMD | Ray Summit 2026
Full-Stack Observability in Ray: Infrastructure Signals in Ray Dashboard | Google | Ray Summit 2026
From Self-Managed Ray to Anyscale Services: Migrating ML Serving | Coinbase | Ray Summit 2026
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Last Updated: October 3, 2026
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Summary
"Github repository: github.com/ Modern models can span thousands of GPUs and months of training. At this scale, failures become the norm, costing GPU hours ... Debugging distributed ML jobs on Kubernetes Coinbase moved production ML serving from self-managed
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