Kubernetes doesn’t know what a Ray cluster is. So teams keep hand writing the same Deployments, Services, and recovery scripts to run Ray on it. KubeRay fixes that: autoscaling, fault tolerance, and zero downtime upgrades for Ray, built into Kubernetes. na2.hubs.ly/H08qhRS0

Heading to @PyTorch Conference next week? ☕ Come hang at the Coffee Club with the teams behind Ray and vLLM, two key projects under the PyTorch Foundation. Hosted by Anyscale, @Inferact and @Nscale. Request your spot here: na2.hubs.ly/H08rrdP0

Run Reflection's Beam at scale in your own account for full sovereignty but without managing infra with Ray Serve LLM on Anyscale!

Open Source AI Week kicks off tomorrow 🎉 📍 The Midway, SF, 6pm 👇 na2.hubs.ly/H08nMYB0 Join us alongside other key players in AI helping organizations adopt and scale their use of open-source.

Anyscale is heading to PyTorch Conference North America, October 20 and 21 in San Jose. Catch our sessions on SkyRL and Ray Core, drop by our Meet the Developers session on DeepSpeed and Ray with the Anyscale team, swing by booth P9, and come recharge with us at the Coffee Club lounge just off the venue. na2.hubs.ly/H08nkrd0

We’re joining Open Together with @huggingface and friends this Friday to support everyone building AI with open-source SF, Oct 16, 6pm 👇 na2.hubs.ly/H08m4Mv0

Our upcoming webinar will cover how to design scalable distributed inference systems for production AI applications, from classic ML models to large language models. Learn how Ray Serve delivers high-throughput, low-latency serving, and how to evaluate the performance tradeoffs in autoscaling, batching, routing, and inference-engine optimization. Topics include: - Building Ray Serve applications with deployments, composition, and load-aware request routing - Deploying and scaling LLM endpoints with Ray Serve LLM on engines like @vllm_project and @sgl_project - Optimizing the inference engine with the KV cache, paged attention, prefix caching, and continuous batching - Scaling frontier models with KV-cache-aware routing, prefill/decode disaggregation, and wide expert parallelism for mixture-of-experts (MoE) models - Deploying Custom LLMs on Anyscale and Integrating Them with Agentic Platforms (Claude Code, Cursor, Codex) - Comparing costs across self-hosted GPUs, subscriptions, and token-based API billing. Register: na2.hubs.ly/H08hfbl0

We’re heading to Long Horizon by Encord next week! 📍Come meet our team at booth P1 and hear from our team: 🎤 From Petabytes to Policies: Scaling the Physical AI Data Engine with Ray 🎤 Why Sim Platforms Shouldn’t Be a Batch Job: Scaling Synthetic Data Generation with Ray na2.hubs.ly/H08jzKS0

People who are really serious about applied AI should train their own models. Sharing our “Owning Intelligence” series on how we built, evaluated, and post-trained a fraud agent at Coinbase—from using frontier models to owning a model optimized for our task. Part 1 — Build lnkd.in/grtg_gfF Part 2 — Evaluate lnkd.in/g-6DKdZ8 Part 3 — Own lnkd.in/g9ET_4at @akshit_trehan @anyscalecompute, @erictang000 , @sumanthrh , @seiji_________ ,

After a year of close collaboration with the Azure team, we're excited to announce the general availability of Anyscale on Azure! Anyscale on Azure is managed Ray on AKS, for the whole loop from data processing to training to inference. Some highlights: - Deploys on AKS in your own Azure subscription - Data and models stay in your subscription - A first-class resource in the Azure portal - Sign in with Microsoft Entra ID - Billed through your Azure invoice, counting toward your existing Azure consumption commitment This is the product of a very deep collaboration with the AKS team and is just the start of what we'll be shipping on Azure. Learn more: anyscale.com/product/azure

New: Anyscale on @Azure, now in GA. Anyscale on Azure is generally available. Run the full AI loop on AKS in your own subscription. Own your AI on open source Ray, keep data and models in your tenant, and get more work from every GPU. Deploy from the Azure portal and pay through your Azure bill. na2.hubs.ly/H08dWKQ0

Excited to announce the release of SkyRL v0.4.0! Some highlights: - Support for GLM 5.3 Flash, GLM 5.3, and Kimi K2.7 - Top-P sampler replay and optimized R3 data transfer - Full FP8/MXFP8 RL - Sharded P2P weight transfer Blog: blog.skyrl.ai/blog/skyrl-v0-… Details in 🧵

If you want to learn more about large scale inference. Check out this webinar. We're covering - How inference engine optimizations work (KV cache, paged attention, prefix caching, continuous batching) - @raydistributed along with @vllm_project and @sgl_project - Custom LLMs & integrating them with agents - Deployment best practices (scaling, routing, composition) - How to compare GPU & token costs anyscale.com/events/2026/11/…

As more teams scale open source AI on secure, governed infrastructure, Ray has become critical across the full AI lifecycle, from data curation to production serving and reinforcement learning. Ahead of PyTorch Conference North America 2026 in San Jose, we put together a guide highlighting key Ray-focused sessions on the schedule. Featured speakers include: Anyscale /@databricks / @UCBerkeley : Ion Stoica @anyscalecompute : Eric Tang, Sumanth Hedge, Josh Lee, Mengjin Yan @Google : Ankita Luthra, Trinadh Kotturu, Jago Macleod @LinkedIn : Tao Huang, Tommy Li @Pinterest : Gaurav Arora, Shunyao Li, Eric Wang @Uber : Ke Chen, Peng Zhang, Xandra Zhu University of Minnesota (@UMNews) : Arun Sharma Explore engineering takeaways from these technical sessions where you will learn about unified AI orchestration with Kubernetes, elastic training stacks, and scalable RL. Read the full guide and map out your schedule: bit.ly/4ysjCku Register for PyTorch Conference North America: bit.ly/464FTbp

Hosting something open-source during OSAI Week? Meetup, workshop, demo — get it on the community calendar. Submissions open now. #OpenSourceAIWeek #OSAIWeekCommunity opensourceaiweek.com/submit-…

Meet the Anyscale team at Fully Connected 2026 at Booth #600 and catch our session on Data Pipeline at Petabyte Scale: Production Video Curation with Ray on CoreWeave na2.hubs.ly/H07Z75-0

In his recent talk at @anyscalecompute's Ray Summit, Bedrock’s Vincent Gonguet covered how the company was able to get to commercial deployments of fully autonomous excavators in only two years, and ways they use Ray to power the stack: heterogeneous CPU and GPU pipelines, mining ambiguous multimodal field data, and a closed-loop simulator that models everything down to the dirt.

If you want the talk version, Ion gave a great talk about gaps in agentic software engineering at Ray Summit. piped.video/watch?v=hATMyrrd…