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

View Presentation

Will Drevo

Matthew Deng

Amog Kamsetty

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

Jules Damji

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

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

Ray logo (webinar thumbnail)

Antoni Baum

Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray was created in the UC Berkeley RISELab to make it easy for every engineer to scale their applications and ML workloads, without requiring any distributed systems expertise, making distributed programming easy. 

Join Jules S. Damji, developer advocate at Anyscale, and Antoni Baum, software engineer at Anyscale, for an introduction to Ray for scaling your ML workloads. Learn how Ray libraries (eg., Ray Tune, Ray Serve, etc) help you easily scale every step of your ML pipeline — from model training and hyperparameter search to inference and production serving.


Scaling Machine Learning Workloads with Ray

Tune

Hyperparameter tuning or optimization is used to find the best performing machine learning (ML) model by exploring and optimizing the model hyperparameters (eg. learning rate, tree depth, etc). It is a compute-intensive problem that lends itself well to distributed execution. 

Ray Tune is a Python library, built on Ray, that allows you to easily run distributed hyperparameter tuning at scale. Ray Tune is framework-agnostic and supports all the popular training frameworks including PyTorch, TensorFlow, XGBoost, LightGBM, and Keras. 

Join this webinar with Will Drevo, product manager for Ray machine learning libraries, for an overview of Ray Tune and demo of using it for tuning a deep learning model. 


Fast and efficient hyperparameter tuning with Ray Tune

ray serve

Tricia Fu

Ray Serve is a framework-agnostic and Python-first model serving library built on Ray. In this introductory webinar on Ray Serve, we will highlight how Ray Serve makes it easy to deploy, operate and scale a machine learning API. 

The core of the webinar will be a live demo that shows how to build a scalable API using Natural Language Processing models. 


Building a scalable ML model serving API with Ray Serve 

survey thumb

Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray was created in the UC Berkeley RISELab to make it easy for every engineer to scale their applications, without requiring any distributed systems expertise. 

Join Robert Nishihara, co-creator of Ray, and Bill Chambers, product lead for Ray, for an introduction to Ray for scaling your ML workloads. Learn how Ray libraries (eg. Ray Tune, Ray Serve, etc) help you easily scale every step of your ML pipeline — from model training and hyperparameter search to production serving. 

__[View Slides >>](https://speakerdeck.com/anyscale/webinar-an-introduction-to-ray-for-scaling-machine-learning-ml-workloads)__

An introduction to Ray for scaling machine learning (ML) workloads

Archit Kulkarni

Seamlessly Scaling your ML Pipelines with Ray Serve

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

Population Based Training with Ray Tune

RSC3

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 headshot

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

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

hero2

Spark-based AI and data platform with Ray

Modernize your Spark-based AI and data platform with Ray on Anyscale

Webinar 73025 1500x1000 2x

Intro to Anyscale: Production-Grade Distributed Computing with Ray

Build Scalable and Production Ready MCP Server Cover

Agentic AI in Practice: Build Scalable and Production Ready MCP Servers

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.

Events