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Anyscale's webinars provide Ray users the opportunity to connect and learn from the global community of Ray experts.

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

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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

Ray Meetup-July 25

Jules Damji

Through a demo using XGBoost for classification, we will demonstrate how you can scale training, hyperparameter tuning, and inference with Ray — all from a single node to a cluster, with tangible improvements in performance.

Ray Meetup @ SF Bay ACM: Scaling ML/AI workloads with the Ray Ecosystem

Ray Meetup-June 22

Gi-Joon Nam

Jinwook Jung

Tomasz Zaluska

It’s time for our June meetup, a monthly series, where we get together to discuss Ray and Ray’s native libraries for scaling machine learning workloads. This month, we have invited Ray community speakers from IBM and Stanford/ETH to share how they use Ray to solve challenging ML problems. Join us to learn about Ray community use cases.

Ray Community Talks: IBM Research and Stanford

Ray Meetup-May 25

Dmitri Gekhtman

Dmitri 

Stanley Bishop

Stanley Bishop-Rosal

Jiaxin Shan

Aniket Maurya

Each month we get together to discuss Ray and Ray’s native libraries for scaling machine learning workloads. For May’s meetup, we have invited Ray community members Stanley Bishop (DeepChem.io), Jiaxin Shan (ByteDance), and Aniket Maurya (Grid.ai) to share how they use Ray to solve challenging ML problems.

Ray Community Talks: Grid.ai, DeepChem.io, and ByteDance

Ray Meetup-Data Science DC

Modern machine learning workloads are compute-intensive and require distributed execution. Ray is an open-source, general-purpose, distributed framework that easily scales Python applications and ML workloads from a laptop to a cluster. 


Ray Meetup @ Data Science DC: Scaling and distributing Python & ML applications with Ray

Webinar-Ray Serve 101

Paige Bailey

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Simon Mo

Edward Oakes

Join this webinar for a primer on Ray Serve - an easy-to-use, scalable model serving library built on top of Ray. 

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Ray Serve 101: Deploying your first ML model locally and as a managed service

Ray Meetup-Ray Serve

Jiao Dong

Shreyas Krishnaswamy

Shreyas

It’s time for April's Ray Meetup, a monthly series where we get together to discuss Ray and Ray’s native libraries for scaling machine learning workloads. This month we will discuss Ray Serve, Ray’s ML framework-agnostic, production-ready, operational, and scalable model serving library.

Productionizing ML at scale with Ray Serve

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RL in production: A roundtable discussion with industry leaders

Webinar-RL roundtable

Richard Liaw

Adam Kelloway

Dr. Marc Weber

Marc Webber

Volkmar Sterzing

Ben Kasper

Reinforcement learning (RL) is the new kid on the machine learning block. Why are industry leaders giving RL a try? When aren’t traditional machine learning and deep learning techniques enough? 


Watch Tutorial

RL-Summit-Tutorial

Sven Mika

Hands-on tutorial on RL and RLlib for building recommendation systems.

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Tutorial: Reinforcement learning for recommender systems

RL-Summit-Siemens

Siemens Technology talk about RL use cases that impact real lives - from washing machines to gas-powered turbines. 

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Reinforcement learning in the physical world

RL-Summit-Dow

Adam Kelloway from Dow talks about AlphaDow, an RL-based industrial production scheduling agent.

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AlphaDow: Leveraging Ray’s ecosystem to train and deploy an RL industrial production scheduling agent

RL-Summit-JPMorgan

Sumitra Ganesh

Sumitra Ganesh from J.P. Morgan talks about using RL for agent-based simulation of markets. 

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Reinforcement learning-based simulation of markets

RL-Summit-Riot-Games

Ben Kasper from Riot Games talks about using RL to help game designers improve game balance. 

 Deep reinforcement learning at Riot Games

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From the creators of Ray, Anyscale is a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab.

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Ray Serve 101: Deploying your first ML model locally and as a managed service

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