Ray RLlib

Ray Train

Ray Datasets

RayDP

Retail

Gaming

Government

Financial

Ray Serve

Anyscale

Ray Core

Healthcare

Technology

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

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

simon

Simon Mo

Ray Serve: Patterns of ML Models in Production

kai and michael

Michael Mui

Kai Fricke

Michael Mui, Senior Software Engineer, Uber ML & Kai Fricke, Software Engineer, Anyscale

Distributed XGBoost on Ray

edi 

Edi Palencia

Edi Palencia, Principal AI Engineer, Microsoft

Offline RL with RLlib

raoul

Raoul Khouri

Raoul Khouri, Quantitative Software Engineer, Two Sigma

A 24x Speedup for Reinforcement Learning with RLlib + Ray

nachi

Nachi Mehta

Ray and Anyscale: An Optimization Journey

pieter

Pieter Abbeel

Pieter Abbeel, Professor, UC Berkeley | Founder, Covariant | Host, The Robot Brains Podcast

Human-in-the-Loop Reinforcement Learning

sarah

Sarah Bird

Sarah Bird, Principal Program Manager, Responsible AI Lead, Emerging Technology & Research Strategy Lead, and Azure AI, Microsoft

Responsible AI at Scale

Ion Stoica, co-founder, executive chairman & president, Anyscale

[Opening Keynote] Making Distributed Computing Easy

robert

Robert Nishihara, co-founder and CEO, Anyscale

[Opening Keynote] State of Ray

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.

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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Thumbnail: Webinar: Getting Started with Distributed Training at Scale

Getting Started with Distributed Training at Scale

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 Live Virtual Hands On Lab: Building Scalable AI with Ray 

Lineage Tracking webinar cover photo

From Unity Catalog to MLflow: Experiment Tracking and Lineage for Ray

Virtual Hands on Lab

 Live Virtual Hands On Lab: Building Scalable AI with Ray

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

Event Types

All Products / Libraries

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Events

Ray Summit

Ray Serve: Patterns of ML Models in Production

Ray Summit

Distributed XGBoost on Ray

Ray Summit

Offline RL with RLlib

Ray Summit

A 24x Speedup for Reinforcement Learning with RLlib + Ray

Ray Summit

Ray and Anyscale: An Optimization Journey

Ray Summit

Human-in-the-Loop Reinforcement Learning

Ray Summit

Responsible AI at Scale

Ray Summit

[Opening Keynote] Making Distributed Computing Easy

Ray Summit

[Opening Keynote] State of Ray

Ray Summit

Adopting Ray @ LinkedIn

Ray Summit

AlphaDow: Reinforcement Learning For Industrial Production Scheduling

Ray Summit

AI In Production Key Challenges and Use Cases

Ray Summit

Ray Summit