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

Time series

Christy Bergman

Amog Kamsetty

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

Will Drevo

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)

Jules Damji

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

Watch tutorial

rllib 

A beginner’s tutorial for working with multi-agent environments, models, and algorithms.

“Hands-on RL with Ray’s RLlib” is a beginners tutorial for working with reinforcement learning (RL) multi-agent environments, models, and algorithms using Ray’s RLlib library. RLlib offers high scalability, a large list of algorithms to choose from (offline, model-based, model-free, etc..), support for TensorFlow and PyTorch, and a unified API for a variety of applications and customizations. This tutorial includes a brief introduction to provide an overview of concepts (e.g. why RL) before proceeding to RLlib models, hyperparameter tuning, debugging, student exercises, Q/A, and more. All code will be provided as .py files in a GitHub repo.


Hands-on Reinforcement Learning with Ray’s RLlib

ray core

Paco Nathan

A Guided Tour of Ray Core covers an introductory, hands-on coding tour through the core features of Ray, which provide powerful yet easy-to-use design patterns for implementing distributed systems in Python. This training includes a brief talk to provide overview of concepts, then coding for remote functions, actors, parallel iterators, and so on. Then we'll follow with Q&A. All code is available in notebooks in the GitHub repo. 


Ray Core Tutorial

Erik E

Erik Erlandson

Erik Erlandson
Software Engineer, Red Hat, AI Center of Excellence

Powering Open Data Hub with Ray

sahika

Sahika Genc

Sahika Genc, Principal Applied Scientist, Amazon

Introducing Amazon SageMaker Kubeflow Reinforcement Learning Pipelines for Robotics

davit

Davit Buniatyan

Davit Buniatyan, Founding CEO, Activeloop

Software 2.0 Needs Data 2.0: A New Way of Storing and Managing Data for Efficient Deep Learning

chris q

Chris Qin

Chris Qin, Senior Software Engineer, Alibaba Cloud

Mars-on-Ray: Accelerating Large-scale Tensor and DataFrame Workloads

amog

Amog Kamsetty, Software Engineer, Anyscale

A Growing Ecosystem of Scalable ML Libraries on Ray

jeroen

Jeroen Bédorf

Ishaan Sood

Jeroen Bédorf (Senior System Architect) & Ishaan Sood (Neural Network and Software Engineer), minds.ai

Using RLLib in an Enterprise Scale Reinforcement Learning Solution

roger

Roger Ng, M.D.

Roger Ng, M.D., Cardiologist, Virginia Mason Heart Institute

The Journey from AI to Bedside: Translating Academic Research to Serving Patients Using Ray

alex

Alex Wu

A Deep Dive into Ray’s Scheduling Policy

stanley

Stanley Bishop

Stanley Bishop-Rosal

Stanley Bishop, Head of Data Science, Drift Biotechnologies

Machine Learning and Microbiology in the Cloud Through Ray

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

hero2

kubecon-2024

Meet Anyscale at Kubecon 2024

anyscale-reinvent2024

Meet Anyscale at AWS re:Invent

ray-singapore-meetup

[Ray Meetup] Exploring the Ray Framework for accelerating AI/ML Applications

ray compiled graphs

[Ray Meetup] Ray Compiled Graphs Deep Dive

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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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