An Overview of CloudKitchens's Ray-Powered ML Platform

Uploaded October 2024 | Updated September 2026, 2 weeks ago

City Storage Systems (CSS) has revolutionized machine learning development with a cutting-edge platform built on Ray and Kubernetes. This session exposes the architecture behind CSS's accelerated ML capabilities, showcasing a system that redefines speed and efficiency in ML operations.

CSS engineers Santosh Jha and Ammar Alrashed reveal the core of their ML infrastructure: a seamless integration of Ray clusters, Metaflow, Daft, and Argo Workflows. Learn how this powerful combination tackles complex scaling challenges and streamlines development processes. For ML engineers and architects seeking to elevate their infrastructure, this session delivers crucial insights into building a next-generation ML platform.

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Interested in more?
- Watch the full Day 1 Keynote: youtu.be/jwZHJthQvXo
- Watch the full Day 2 Keynote youtu.be/Lury2ad6KG8

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An Overview of CloudKitchenss Ray-Powered ML Platform | Ray Summit 2024

$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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