Anyscale on Azure: Build and deploy AI at scale in your own tenant @anyscale

Uploaded June 2026 | Updated September 2026, 2 weeks ago

Anyscale on Azure is now in public preview. Built by the creators of Ray, the most widely adopted AI compute engine, it runs inside your own Azure tenant so your data stays within your existing governance, and it gives teams a production-ready platform to build, train, and serve AI without stitching infrastructure together.In this session, you'll learn:

What Anyscale on Azure is and where it fits in your AI stack

How it integrates with the Azure services you already use, including 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

A live demo of building, training, and serving a real AI workload on Anyscale in an Azure environment

Whether you're a platform engineer, ML engineer, or architect deciding where to run AI on Azure, you'll leave knowing how Anyscale on Azure works and how to get up and running.

Anyscale on Azure: Build and deploy AI at scale in your own tenant

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$Prompt Learning: A Reinforcement Learning-Inspired Approach to AI Optimization | Ray Summit 2025 At Ray Summit 2025, Jason Lopatecki from Arize AI shares a new paradigm for iterative model improvement—Prompt Learning (PL)—a technique inspired by reinforcement learning but performed entirely through prompts, without weight updates, large datasets, or gradient-based training. He begins by framing the central question: What if we could achieve RL-like gains in model performance simply by refining prompts using natural-language feedback? Building on ideas from NVIDIA’s Voyager and broader community discussions, Arize explores how structured English feedback—evaluations, explanations, annotations—can act as an “error term” that guides models toward better behavior. Jason walks through the conceptual foundation of Prompt Learning and why encoding corrections directly in language can dramatically reduce data requirements—sometimes down to only a handful of examples. Key highlights include: The Prompt Learning framework: How PL adapts RL-style improvement loops to the prompt layer Why natural-language feedback is powerful: Turning textual “error signals” into prompt refinements without extra training PL vs RL efficiency: A head-to-head comparison showing where PL outperforms traditional RL-driven refinement Real-world experiment: Guiding JSON generation with latent constraints using only a few examples TextPRO system: Arize’s PL engine that performs end-to-end optimization through single LLM calls—at a fraction of the supervision cost Jason shares experimental results showing that Prompt Learning can achieve up to 70% test accuracy using just five feedback rules, outperforming standard prompting techniques and demonstrating that lightweight, language-based optimization can yield surprisingly strong results. Attendees will leave with a new lens on iterative LLM improvement—one that opens the door to powerful, low-cost prompt optimization without full RL pipelines or large training sets. Liked this video? Check out other Ray Summit breakout session recordings https://www.youtube.com/playlist?list=PLzTswPQNepXllnU0C36WtkC0dqkAoDulh Subscribe to our YouTube channel to stay up-to-date on the future of AI! https://www.youtube.com/c/anyscale 🔗 Connect with us: LinkedIn: https://www.linkedin.com/company/joinanyscale/$

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