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

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

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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

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

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

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)

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

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

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.

Learn why Anyscale is best place to run Ray

Free, self-paced curriculum for practitioners

One-click to launch. Start fast, make it yours

Events

Ray Meetup

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

Webinar

Ray Datasets: Scalable data preprocessing for distributed ML

Ray Meetup

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

Webinar

Ray Train: Production-ready distributed deep learning

Webinar

Introduction to Ray Core and its Ecosystem

Ray Meetup

Ray Meetup is Back in the New Year 2022

Webinar

Faster time series forecasting at scale using Ray and Anyscale

Webinar

Introducing Anyscale: Accelerate scaling and deployment of AI / ML

Webinar

RLlib: Scalable RL training and serving in the cloud with Ray

Webinar

Scaling Machine Learning Workloads with Ray

Webinar

Fast and efficient hyperparameter tuning with Ray Tune

Webinar

Building a scalable ML model serving API with Ray Serve

Webinar

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

Anyscale Academy

Hands-on Reinforcement Learning with Ray’s RLlib

Anyscale Academy

Ray Core Tutorial

Ray Summit

Powering Open Data Hub with Ray

Ray Summit

Introducing Amazon SageMaker Kubeflow Reinforcement Learning Pipelines for Robotics

Ray Summit

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