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Multi-Modal Data Processing Pipelines for AI Systems | Anyscale | Ray Summit 2026
Faster and Cheaper Offline Batch Inference with Ray
Last Mile Data Processing for ML Training using Ray
Large Scale Data Loading and Data Preprocessing with Ray
How to Scale Unstructured Data Processing with Ray Data | Ray Summit 2024
Ray Data: Scalable AI Computing & Distributed Systems
TALK / SangBin Cho / Data Processing on Ray
From Spark to Ray: CSS's Data Revolution with Daft | Ray Summit 2024
Ray Data Streaming for Large-Scale ML Training and Inference
High-Throughput Inference for Synthetic Data & Evals at Sutro | Ray Summit 2025
Scaling Multi-Modal Datasets to Petabytes with Ray at Apple | Ray Summit 2025
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Last Updated: September 27, 2026
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(SangBin Cho, Anyscale) Machine learning and In ML your model is as good as your Don't the Sound Effect?:* youtu.be/zVy49qu9KbE *Text:* ... "Github repository: github.com/anyscale/ In this talk, we will discuss how Pinterest integrated Goutam Venkatramanan, Software Engineer at Anyscale, introduces City Storage Systems (CSS) revolutionizes its machine learning infrastructure by embracing Daft, a powerful DataFrame library ... Some of the most demanding ML use cases involve pipelines that span both CPU and GPU devices in distributed environments.