An introductory, 5-page guide on the basics of Ray - what is Ray Core vs. Ray Libraries - plus a quick introduction to Anyscale and resources to help you continue on your Ray journey

Ebook: Ray and Anyscale Primer

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Ray Primer: What is Ray, Ray Libraries (Data, Train, Serve) & More

Intro to Ray: The Distributed Compute Framework for AI

MLOps LLM Survey Report - eBook

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Download the eBook

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Access Anyscale today to see how companies using Anyscale and Ray benefit from rapid time-to-market and faster iterations across the entire AI lifecycle.

MLOps LLM Survey Report

Read the eBook to learn how Anyscale and AWS offer benefits like improved scalability, unified development and production environments, and an easy path to operationalizing workloads.


Simplify ML Development, Scaling, and Deployment

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How to Simplify ML Development, Scaling, and Deployment in Production

Figure 1: AI computing application scenarios in Weixin

The architecture of a Reinforcement Learning (RL) library is split into two primary components: Generation and Training. During the generation phase, an LLM Engine performs multi-turn rollouts within an environment to produce data and reward signals. This output is then fed into the training phase to update the model's parameters. This process forms a feedback loop, where the progressively improved model generates the next iteration of data for continuous refinement.

Open Source RL Libraries for LLMs Graph

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AI Application Platform Buyers Guide

eBook AI Platform Buyer Guide

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AI Application Platform Buyer’s Guide

Learn more about the Anyscale platform, explore expert insights, and stay on top of the latest trends.


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Powered by Ray, Anyscale empowers AI builders to run and scale all ML and AI workloads on any cloud and on-prem.

Resources

eBook

Intro to Ray: The Distributed Compute Framework for AI

eBook

MLOps LLM Survey Report

eBook

How to Simplify ML Development, Scaling, and Deployment in Production

eBook

Ebook Landing Page Test

eBook

AI Application Platform Buyer’s Guide