Ray RLlib

Ray Train

Ray Datasets

RayDP

Retail

Gaming

Government

Financial

Ray Serve

Anyscale

Ray Core

Healthcare

Technology

Ray Summit brings together the community of architects, machine learning engineers, researchers, and other developers building the next generation of scalable, distributed, high-performance Python and machine learning applications. The conference will include those who are already using Ray platform and those who are interested in learning how Ray can meet their needs.

Ray Summit

Ray Roadshow

Here you can find where the Anyscale and Ray teams will be in attendance at industry events across the globe. 

Industry Event

Press Interview

On-Demand Webinar

Webinar

RL Summit

Weekly Demo

Ray Meetup

Anyscale GA Launch

Anyscale Academy provides training on the Ray ecosystem, tailored for data scientists, data engineers, and MLOps engineers.

Anyscale Academy Logo

Anyscale Academy

Roger Ng, MD

Roger Ng

Transforming Medicine - Machine Learning Takeover

Ameer Haj Ali

A Glimpse Into the Ray Autoscaler

Chris Nicholson

AI Use Cases in Industrial Operations and Supply Chain

Anyscale logo

Dan Wallach

The design of Arlo-e2e (and how Ray helped save the day).

Encrypting and Tabulating Big Elections

Ray Summit Connect

Amog Kamsetty

Population Based Training with Ray Tune

LinkedIN RS20

Jonathan Hung

Nitin Pasumarthy

In this talk, we will present our effort to run Ray on YARN, and the integration of Ray on LinkedIn’s open-sourced offline infrastructure: Azkaban (workflow scheduling service) and TonY (Tensorflow on Yarn). We will provide a demo of running a Ray job end-to-end, discuss the architectural decisions and talk about our cooperation with the Ray team on this effort.
In the later half of the talk, we will share how we used Ray Tune on Kubernetes in a real world use case. Tune helped us identify promising model configurations, using state of the art Bayesian optimization algorithms like TPE & PBT, with minimal supervision. The data pipeline is also optimized using Tune to extract the maximum throughput and we were able to train 2x faster by reducing the GPU idle times.

Adopting Ray @ LinkedIn

dow rs20

Adam Kelloway

Adam K

Adam has deployed reinforcement learning trained agents that optimize a production planning problem at Dow, Inc and deliver measurable business impact. Inspired by the successes of AlphaGO, this ambitious project is named AlphaDow. The production scheduling problem at Dow is non-trivial. Dow’s continuous manufacturing plants transition through a product sequence which must minimize the costs of transitions and maximize product availability for customers all whilst minimizing inventory on hand. Human schedulers use experience and heuristics to select the best sequence today. The trained RL agents augment this decision-making process. Adam has used Ray extensively to distribute his training among Azure based compute clusters. This significantly decreased the time needed to train an RL agent capable of producing optimal production schedules. Adam will present several of the challenges faced when training and deploying RL agents for industrial production scheduling and how Ray has helped him to overcome those challenges by facilitating the scale required to solve these complex problems and achieve real-world business impact.

AlphaDow: Reinforcement Learning For Industrial Production Scheduling

berte

Jonathan Berte

Scaling AI applications require a parallel pipeline approach. With the help of ray actors, Robovision created the concept of ML framework independent compute cells and pipelines. These cells can be reconfigured later into other pipelines and be published in a private or public AI store. The age of scalable AI has finally started. We explore scalable AI cases in agritech, healthcare, manufacturing and smart cities.


AI In Production Key Challenges and Use Cases 

RaySummit Banner Virtual Snackable

Ray Summit is about Ray, the open-source Python framework for building distributed applications that run at any scale. This inaugural, two-day conference showcases Ray best practices, real-world case studies, and the latest research in AI and other scalable systems built on Ray.

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Ray Tune and Ray Serve

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Eric Liang

Eric Liang headshot

Anna Luo

Anna Luo headshot

Max Pumperla

Max Pumperla headshot

Practical Reinforcement Learning

Richard Liaw

Richard Liaw headshot

Liam Li

Liam Li headshot

Meghana Ravikumar

Meghana Ravikumar headshot

The Road to AutoML: Hyperparameter Tuning and Neural Architecture Search

Mike-Jordan

Michael I. Jordan

Michael I. Jordan headshot

Ion Stoica

Ion Stoica headshot

The Future of ML and AI with Michael Jordan and Ion Stoica

Live Virtual Hands On Lab: Distributed Training at Scale with Ray and PyTorch

A one stop shop for all events, past and future, to connect and learn from the global community of Ray experts.

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Ray Summit 2026

Meet the builders, platform leads, and researchers training foundation models, building multimodal pipelines, and running RL at scale.

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

See how Anyscale on Azure enables distributed AI training, inference, and data processing with Ray while integrating with Azure services and governance. 

How Torc Robotics Scales Multimodal AI for Autonomous Driving with Ray

Building a Multimodal Video Processing Pipeline with Ray

Multimodal data - Architecting pipelines that don’t break at scale

Multimodal data: Architecting pipelines that don’t break at scale

This section is used to order the "Types" and "Tags" that show up for filters on the Event Index

Event Types

All Products / Libraries

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

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Events

Events

See what AI teams can build

Transforming Medicine - Machine Learning Takeover

A Glimpse Into the Ray Autoscaler

AI Use Cases in Industrial Operations and Supply Chain

Encrypting and Tabulating Big Elections

Population Based Training with Ray Tune

Adopting Ray @ LinkedIn

AlphaDow: Reinforcement Learning For Industrial Production Scheduling

AI In Production Key Challenges and Use Cases

Ray Summit

Ray Tune and Ray Serve

Practical Reinforcement Learning

The Road to AutoML: Hyperparameter Tuning and Neural Architecture Search

The Future of ML and AI with Michael Jordan and Ion Stoica

Live Virtual Hands On Lab: Distributed Training at Scale with Ray and PyTorch

Live Virtual Hands On Lab: Distributed Training at Scale with Ray and PyTorch