Thanks to our Ray Day: London speakers: Marcell Ferencz (@Xoople), Martin Iglesias & Maxime Battello (@Adyen), Paul Coursaux (@Criteo), and Thomas Riedl (@BMWGroup), plus a keynote from @pcmoritz. Recap 👉 na2.hubs.ly/H065bcv0 The road leads to Ray Summit SF, Aug 24–26 → na2.hubs.ly/H0659wN0

Thank you to our Ray Day: NYC speakers 🔆 Serrana Aguirregaray (@discord), Neil Wadhvana (@torc_robotics ), Todd Gaugler (Cubist), and Aman Choudhary (@coinbase) brought four different takes on Ray in production, from ML platforms to quant finance. Highlights from all 4 talks 👉 na2.hubs.ly/H064bc-0

My vLLM pipeline wouldn't start. KV cache came out negative on the L4. One prompt to /anyscale-platform-fix and the agent takes it from there. Four minutes to debug instead of a full afternoon. Full walkthrough: na2.hubs.ly/H062c-V0

Ray Summit 2026 keynote lineup is coming together. @LiamFedus (@periodiclabs, prev. co-creator of ChatGPT), @_FelixHeide_ (@torc_robotics), @real_ioannis (@reflection_ai), @kevinmpeterson1 (@BedrockRobotics), @robertnishihara & @istoica05 (Anyscale). ⏰ CFP closes June 20: na2.hubs.ly/H062cPc0

Thanks for joining Ray Day: NYC! Two tracks — distributed training with Ray PyTorch and VLA fine-tuning — plus talks from @discord, @torc_robotics, @coinbase & Cubist. Recap → na2.hubs.ly/H062d0F0  Next: Ray Summit SF, Aug 24–26 →na2.hubs.ly/H062gYk0

Anyscale on Azure is now in public preview, and we're going deep on how it works. Join Daniel Arrizza (Field Engineer, Anyscale) and Paul Yu (Senior Cloud Advocate, Microsoft) for a working session on running production AI inside your own Azure tenant – where your data stays within your existing governance. You will learn: - Where Anyscale on Azure fits in your AI stack - How it integrates with the Azure services you already use: Microsoft Entra ID, Azure RBAC, Azure Policy, Azure Monitor, and Microsoft Cost Management - What you need to get started, from your Azure tenant to your first Ray cluster - Plus a live demo: building, training, and serving a real AI workload on Anyscale in an Azure environment na2.hubs.ly/H061t3b0

Last chance to sign up for tomorrow's webinar! Neil Wadhvana, Staff ML Engineer at @torc_robotics, will walk through how Torc consolidated its autonomy data processing stack to support multimodal AI at scale with Ray on Anyscale. Don't miss the opportunity to learn: - The trends driving growth in autonomous driving developments, - An overview of Torc’s data loop from production to consumption, - The internal trends in multimodal AI that drove need for consolidation, - The before and after Ray was adopted as common compute framework. na2.hubs.ly/H05Dgrf0

Impressive work from the Cosmos team. Check out the Cosmos 3 technical report: arxiv.org/pdf/2606.02800

Congratulations to @nvidia on the release! Super exciting to see two models trained with Ray back to back (MAI-Thinking-1 and Nemotron 3 Ultra).

Nemotron 3 Ultra is an impressive 550B parameter (55B active) MoE model. It was trained with Ray / Megatron / vLLM (via NeMo RL).

GPUs in Mumbai, training data in Iowa? Cross-region reads tax every epoch. We put @Alluxio NVMe caching in front of the bucket with Ray Data on Anyscale: 1TB warm reads went 20x faster. na2.hubs.ly/H05YMGW0

The bottleneck in drug discovery isn't designing molecules, it's making them. onepot combines robotic synthesis with large-scale ML inference on Anyscale to predict which reactions will work before they run, achieving 3B compounds and 10B reactions scored. Case study: na2.hubs.ly/H05PcCG0"

Very impressive work from @MicrosoftAI.

Congratulations to the Microsoft AI team on MAI-Thinking-1! Exciting to see Ray used in multiple parts of frontier-model development. - Fast pre-training recovery via in-job restarts with hot standbys - Async RL orchestration (managing learners, inference servers, rollout workers, and routers, each with distinct placement and fault-tolerance needs) - A two-pool Ray cluster for building and grading SWE environments on 30K CPU cores

It's interesting to see @MicrosoftAI uses ray actors not just for controller and rollout workers but problem workers for the posting training of the MAI-Thinking-1 model. Instead of introducing third party dependency like @modal for sandboxing, Ray actors could provide finer granularity and control for heterogeneous compute which could translate to better utilization of the unused CPU resources in the GPU cluster and easier communication of the agent execution results. Also the part of work I did was to support @sgl_project with @raydistributed backend to better support RL infra especially in weight syncing.

🎬 New on Azure Friday: scaling Python AI workloads with managed Ray on AKS. @shanselman talks with Anyscale's Omar Shorbaji on running build-train-serve on Ray, directly on AKS — no Kubernetes wrangling. Plus a live demo fine-tuning a robotics policy. Catch it here 👇 na2.hubs.ly/H05VrTC0

The co-founder of Kubernetes meets the co-creator of Ray. @brendandburns and @robertnishihara on Anyscale on Azure — why AI belongs on AKS, and what it unlocks: a production layer for Ray, Azure Native, with 4x faster dev and 50% higher GPU utilization. Watch 👇

📣 Webinar: Anyscale on Azure — build and deploy AI at scale in your own tenant. 📅 Tue June 16, 8:30 AM PDT Live with Daniel Arrizza (Anyscale) and Paul Yu (Microsoft): how to run production AI inside your own Azure tenant, plus a live build-train-serve demo. Register 👉 na2.hubs.ly/H05VrZ80

Today at #MSBuild, Anyscale on Azure is now in public preview. Enterprises can run the full AI lifecycle inside their own Azure environment — data, models, and pipelines stay in your tenant, with governed compute instead of per-token API costs. Learn more here: na2.hubs.ly/H05StJS0

New: Anyscale on Azure, now in public preview. Build AI on your own data, inside infrastructure you govern — sovereign AI. It's how Wayve and Xoople train production AI, from autonomous driving to satellite imagery. na2.hubs.ly/H05StVb0