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Hi Thomas,
AI systems are becoming more capable, but also more complex to build, debug, and scale.
This month, we’re highlighting how teams are using Ray on Anyscale to move faster across modern AI workloads, from improving developer velocity with agents, to architecting petabyte+ multimodal pipelines, to scaling physical AI and vision-language reinforcement learning.
Build, debug, and optimize your AI workloads quickly with Anyscale Agent Skills
As AI workloads become more distributed, it gets harder to pinpoint bottlenecks, trace failures, and improve performance without losing engineering time. Anyscale Agent Skills for Ray introduces three specialized agents: Workload Agent, Platform Agent, and Infra Agent, to help teams generate code, debug issues faster, and optimize how AI workloads run on Ray.
The announcement also introduces the Optimization Services Program, a limited access engagement with the Anyscale field team. The program extends Agent Skills with deeper workload analysis across GPU cost and utilization, throughput, and system bottlenecks to help teams reach higher cost-performance efficiency.
Read the announcement →
Webinar: Architecting and deploying multimodal pipelines at scale
Multimodal systems push teams beyond traditional data processing engines. In production, pipelines using AI often need to coordinate large scale data processing, distributed compute, and multiple stages of inference across text, images, video, and other unstructured data types.
Join us on May 14 for a session on how to architect multimodal pipelines that can scale reliably in real world environments. We will cover the infrastructure patterns, orchestration considerations, and deployment approaches teams are using to move from prototype to production.
Register for May 14 event →
In-Person workshops: Pittsburgh, Boston, New York and London are next
Anyscale is bringing hands-on Ray workshops and community days to cities across North America, featuring talks from engineers at Discord, Uber, Coinbase, Notion, Salesforce, and more.
Join us in one of our upcoming stops, where our field engineering team will lead a hands-on workshop on running AI scale. Choose from fine-tuning VLA and distributed Pytorch workshops.
Register for an event near you →
Read the recap blog on Ray user talks from Ray Day Seattle →
How Multiply Labs Advances AI for Biologics Robotics on Anyscale
Multiply Labs is building robotics systems for biologics manufacturing, where teams need to train and deploy models quickly across heterogeneous infrastructure. That means scaling across large datasets, continuous experimentation, and a mix of GPU types and cloud environments.
In this case study, see how Multiply Labs uses Anyscale to accelerate AI development and support production robotics workloads, including achieving 3x faster spin up of NVIDIA H100, A100, and RTX 6000 GPUs across AWS, Azure, and Nebius.
Read full case study →
Introducing Vision-Language Reinforcement Learning in SkyRL
Visual agent workloads such as robotics and computer use require post training systems that can handle images and actions reliably at scale. A major challenge is keeping inference and training aligned, since even small differences in multimodal input processing can create log probability inconsistencies and destabilize reinforcement learning.
Explore how SkyRL uses vLLM as the source of truth for multimodal input preparation, helping teams run more stable vision language post training while scaling data processing separately to keep GPUs fully utilized.
Read the blog →
Ready to build your own Physical AI pipeline with Anyscale Agent Skills? Install in seconds via the Anyscale CLI:
anyscale skills install --platform claude-code
# or
anyscale skills install --platform cursor
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