Ray RLlib

Ray Train

Ray Datasets

RayDP

Retail

Gaming

Government

Financial

Ray Serve

Anyscale

Ray Core

Healthcare

Technology

Ray Tune

Webinar

Summit

RL Summit

Weekly Demo

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

Anyscale flavicon new white

Anyscale Connect

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

blog-challenges-of-deploying-ml-thumb

Richard Liaw

Richard Liaw headshot

Phi Nguyen

Don't miss out on the October 12, at 11am PT live webinar hosted by Anyscale to learn how to simply scale any AI or Python workload using Ray, the fastest growing unified compute framework for scaling machine learning workloads.

Learn how users are using Ray to building, managing and deploying scalable ML workloads with:

- __Instant workload scaling__ from laptop to cloud with minimal code changes and a single script for data preparation, train, tune and deploy ML workloads. 
- __Batch training and tuning__ with 9-22x performance enhancements when compared to alternatives. 
- __Unstructured data processing__ simplified from ingestion for complex datasets including images, transcripts, text, documents, logs, audio and video at scale. 
- __Large-scale inference and simulation__ through embarrassingly parallel experiments such as drug discovery, simulation-based experiments and backtesting at scale. 

Sign up and learn how you can accelerate your time-to-value with Ray!


Simplify Scaling Machine Learning and Python Workloads

Ray Meetup September

Ray Meetup Community Talks

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

TEST

test

Test event 3

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

paige-headshot

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. 

ray serve

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

firstname

lastname

email

RL in production: A roundtable discussion with industry leaders

Webinar-RL roundtable

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.

---


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. 

---

Reinforcement learning in the physical world

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

hero2

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

Event Types

All Products / Libraries

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

Events

Scalable AI and Python

Featured Events

Webinar

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

Latest Events

Ray Meetup

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

Ray Meetup

Ray Community Talks: IBM Research and Stanford

Ray Meetup

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

No events found

Product

Learn

Company