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

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

Dr. Marc Weber

Marc Webber

Volkmar Sterzing

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

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

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

 Deep reinforcement learning at Riot Games

RL-Summit-Keynote

Sergey Levine

Opening Keynote on Offline RL by Sergey Levine. 

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Keynote: Offline reinforcement learning

firstname

lastname

email

Register

Webinar-XGBoost

Phi Nguyen

Antoni Baum

In this webinar, we will cover how Ray, a universal distributed computing framework running on Anyscale, simplifies the end-to-end machine learning lifecycle and provides serverless compute without limits. We will go through an example from beginning to end using XGBoost.

Simplify and scale your XGBoost model using Ray on Anyscale

Ray Meetup-Ray Train

Will Drevo

Amog Kamsetty

Matthew Deng

Mark Saroufim

Welcome to our second Ray meetup, where we focus on Ray’s native libraries for scaling machine learning workloads. 

We'll discuss Ray Train, a production-ready distributed training library for deep learning workloads. And will present TorchX and Ray Integration. Through this integration, PyTorch developers can submit PyTorch-based scripts and workloads to a Ray Cluster using TorchX’s SDK and CLI via its new Ray Scheduler.

Ray Train, PyTorch, TorchX, and distributed deep learning

Webinar-Getting started with Anyscale

Charles Greer 

Javier Redondo

In this webinar, the Anyscale solutions team will guide you through the steps of taking your Python workloads to the cloud. Previous knowledge of Ray is helpful but not required as we demonstrate the concepts behind running distributed loads in the cloud, and how to interact with Ray and Anyscale in your Python code. 

Getting started with Anyscale concepts, code, and clusters

Ray Meetup-BAyPIGgies

Jules Damji

Today, AI applications are becoming pervasive across all sectors of our industry. Driven by a few fundamental trends, there is no indication of slowing down. In fact, the trend continues rapidly, making distributed computing at scale a norm and necessity.

Ray Meetup @ BAyPIGgies: Scaling AI workloads with the Ray Ecosystem

Webinar-Ray Datasets

Clark Zinzow

Alex Wu

Ray Datasets is a Ray-native distributed dataset library that serves as the standard way to load, process, and exchange data in Ray libraries and applications. It features performant distributed data loading, flexible parallel compute operations, and comprehensive datasource compatibility and distributed framework integrations, all behind an incredibly simple API.

Ray Datasets: Scalable data preprocessing for distributed ML

Ray Meetup-Data+AI Denver

Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray is an open-source, distributed framework from U.C. Berkeley’s RISELab that easily scales Python applications and ML workloads from a laptop to a cluster, with an emphasis on the unique performance challenges of ML/AI systems. It is now used in many production deployments.

Ray Meetup @ Data + AI Denver Meetup: Ray, a framework for scaling and distributing Python & ML applications

View Presentation

Today, most frameworks for deep learning prototyping, training, and distributing to a cluster are either powerful and inflexible, or nimble and toy-like. Data scientists are forced to choose between a great developer experience and a production-ready framework. 

To fix this gap, the Ray ML team has developed Ray Train. 

Ray Train: Production-ready distributed deep learning

Watch the recording

Webinar-Intro to Ray Core

This is an introductory and interactive session on concepts and code on Ray Core and Ray’s Ecosystem. The session covers why use and adopt Ray, touches upon Ray’s Ecosystem for scaling machine learning (ML) workloads, and provides an overview of Ray’s architecture and components.

We’ll also cover Ray Core APIs to write remote functions, actors and understand Ray’s basic design patterns for writing distributed Python applications. 

By the end of this session, you will learn:
- Why distributed computing is a necessity today
- What Ray native libraries to use to scale your ML workloads
- How to use Ray Core APIs


Introduction to Ray Core and its Ecosystem

Welcome to our first Ray meetup in the New Year 2022, after two years of absence due to the Covid-19 pandemic. We are super excited to be back.


Ray Meetup is Back in the New Year 2022

Time series

Christy Bergman

Forecasting is an important part of running any business.  The “embarrassingly parallel” pattern of distributed computing works with time series algorithms such as ARIMA, Prophet, and Neural Prophet.  For global deep learning algorithms, both data and model parallelism become simple and easy with Ray.  

In this webinar, we will demo both patterns.  For the deep learning algorithm, we will show how to take Google’s Temporal Fusion Transformer, available in PyTorch Forecasting, and turn training and inference into distributed applications using Ray.  Ray code runs in parallel across local laptop cores.  The exact same code can run in parallel across any cluster in any cloud too.

Faster time series forecasting at scale using Ray and Anyscale 

Watch on-demand

scale-laptop-to-cloud

Robert Nishihara

Ion Stoica

Bill Chambers

Tobias Kaymak

We are excited to [announce the general availability (GA)](https://www.anyscale.com/blog/the-future-is-distributed) of the Anyscale managed Ray platform. 

We created Ray to enable every developer to effortlessly scale their AI/ML workloads from the laptop to the cloud without needing to built and manage complex infrastructure, or become a distributed systems expert. The Anyscale platform builds on that vision and brings the power of instant scaling on Ray to a much larger community. 

__Join our special Anyscale GA launch event with Ray creators and Anyscale founders, Robert Nishihara and Ion Stoica,__ for an overview and live demo.

__Session highlights include:__
- __Infinite laptop experience__: Accelerate development and experimentation through instant access to cloud scale with a laptop-like experience. 
- __Production jobs & services:__ Effortlessly deploy AI workflows and models into production
- __Unified compute:__ Manage the full AI lifecycle on a single  platform

Can't attend live? Sign up anyway, and we will send you a recording after the event. 

Learn more about the announcement: [anyscale.com/blog/the-future-is-distributed](https://anyscale.com/blog/the-future-is-distributed)

Introducing Anyscale: Accelerate scaling and deployment of AI / ML

Rl lib thumb

RLlib on Ray is an industrial-strength reinforcement learning (RL) framework with the power of Ray autoscaling built in. 

Get an overview of RLlib, and learn why organizations like Wildlife Studios and Dow Chemicals are using it to apply RL to business problems like recommendations and supply chain optimization. 

This webinar will also show how to set up an environment, train a model, and deploy to an HTTPS endpoint to serve your policy in production.


RLlib: Scalable RL training and serving in the cloud with 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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ray compiled graphs

[Ray Meetup] Ray Compiled Graphs Deep Dive

Supercharging AI Platforms with Anyscale

Optimized AI Workloads on Anyscale with RayTurbo

Call for Proposals is open Blog Post

Ray Summit 2024

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Anyscale is the leading AI application platform. With Anyscale, developers can build, run and scale AI applications instantly.

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