ray

@Notion is scaling AI search for millions of users. Mickey Liu shares how moving from Spark to Ray Data on Anyscale helps generate embeddings across terabytes of data, with CPU and GPU workloads in one pipeline. Watch: na2.hubs.ly/H08TrWn0

Anyscale is at #KubeCon in Salt Lake City this week. Find us at booth #P317, join KubeRay 101 on Nov 11, and catch Rueian Huang on The Scheduling Stack panel on Nov 12. na2.hubs.ly/H08TtmM0

📍Nov 17-20 we're at Microsoft Ignite, Moscone SF. Booth 5389L. The next wave of foundation models is coming from enterprises. Every company needs at least one FM it controls: open model, post-trained on your data, on your infra. Session Nov 18 (THR900). Learn more: na2.hubs.ly/H08V4YK0

Using Ray on Kubernetes? Try a streamlined approach to run observability and development with KubeRay Connect. New accounts get $500 in Anyscale Platform credits (limited time offer) to test out KubeRay Connect today! 👉 Claim your $500 credits

Big news at KubeCon Americas. KubeRay is joining the @CloudNativeFdn Keep your KubeRay stack and add the Anyscale Control Plane in minutes. Find us at #KubeCon booth #P317.  na2.hubs.ly/H08TY0t0

@Mind_Robotics trains robot foundation models on egocentric video captured on automotive production lines. On Nov 19, they'll present what works and what doesn't. Meetup is packed, but we saved a few seats for last-minute registrations: na2.hubs.ly/H08RLpt0

Physical AI Night is back on Nov 17 in SF! We’re teaming up with @Foxglove & @nebiusai to hear from leaders at @BedrockRobotics, @DexterityInc, @wayve_ai & @ThirdWaveAi on the frontier of robotics and autonomy. Save your seat: na2.hubs.ly/H08Qb7p0

For post-training AI for science, a genome doesn't fit in a 16k context window. So we went to 1M. To solve this, the SkyRL team at Anyscale worked with @LilaSciences to post-train GLM-5.3-Flash at 1M tokens per sequence on 64 B200 GPUs, using a fully open-source stack. Learn more: na2.hubs.ly/H08Q9vw0

In this session, @erictang000, Software Engineer at Anyscale, will walk through: - Intro to LLM post-training - Intro to RLVR (with GRPO) - Recent development of LLM post-training:     - On-policy distillation (OPD) and Multi-teacher On-policy distillation (MOPD) - Case study: Post-training a terminal agent     - Building evals     - Building custom environments     - RL systems - Demo walkthrough     - Intro to SkyRL     - Full Async RL with SkyRL     - Performance monitoring and tuning for RL rollouts Register: na2.hubs.ly/H08wNDx0

Post-training is now a core workload for AI labs, data providers, and enterprise leveraging proprietary data to build custom models. Learn from the practictioners doing it at @harborframework, @NovaSkyAI and @mercor Doors open 5:30 PM 🍕 na2.hubs.ly/H08MNd-0

A failed GPU job used to be a quick kubectl logs. Then a second team joins, and the cluster that had the answer is already gone, GPUs still billing on the rerun. Six field-tested KubeRay practices for that moment, and where KubeRay Connect picks up: na2.hubs.ly/H08M5LS0

Anyscale is at #KubeCon in Salt Lake City next week. Find us at booth #P317, join KubeRay 101 on Nov 11. na2.hubs.ly/H08M3Q90

Last chance to sign up for next week's webinar! Don't miss the opportunity to learn about large scale inference: - How inference engine optimizations work (KV cache, paged attention, prefix caching, continuous batching) - Ray Serve 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 Register: na2.hubs.ly/H08jzDW0

Most speech AI is built to understand intent. @StimulerApp needs to know exactly what was said, phoneme by phoneme, to coach non-native English speakers in real time. Custom models. Four independent stacks. No Kubernetes. Three ML engineers. Full case study: na2.hubs.ly/H08FDlm0

Physical AI gets tested in fog, rain, snow, and glare, where data is scarce. Our NeurIPS 2026 lesson restages one real drive in bad weather with NVIDIA Cosmos Transfer 2.5 + Ray, check out the interactive app: na2.hubs.ly/H08DsnD0

Multi-band satellite imagery left @Xoople's GPUs idle, epochs running 5x slower than they should. Fix: Anyscale FDE tuning on the CPU-to-GPU handoff + fractional GPU allocation across A10s. 15 engineers at planetary scale with no infra team: na2.hubs.ly/H08qgSQ0

Learn how to productionize large language models using distributed post-training and inference optimization techniques. Participants will explore advanced LLM post-training techniques, including agentic reinforcement learning with SkyRL. Hands-on topics include: - Intro to LLM post-training - Intro to RLVR (with GRPO) - Recent development of LLM post-training:     - On-policy distillation (OPD) and Multi-teacher On-policy distillation (MOPD) - Case study: Post-training a terminal agent     - Building evals     - Building custom environments     - RL systems - Demo walkthrough     - Intro to SkyRL     - Full Async RL with SkyRL     - Performance monitoring and tuning for RL rollouts Register: na2.hubs.ly/H08wNCZ0