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

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RL in production: A roundtable discussion with industry leaders

Webinar-RL roundtable

Paige Bailey

paige-headshot

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? 


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

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

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

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

Rl lib thumb

Sven Mika

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

Ray logo (webinar thumbnail)

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

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

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