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Ameer Haj Ali

Anyscale Connect: A Glimpse Into the Ray Autoscaler

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Chris Nicholson

Anyscale Connect is an hour long speaker series, focusing on topics related to the Ray Open Source project.

Anyscale Connect: AI Use Cases in Industrial Operations and Supply Chain

Dan Wallach

The design of Arlo-e2e (and how Ray helped save the day).

Encrypting and Tabulating Big Elections

Ray Summit Connect

Amog Kamsetty

Population Based Training with Ray Tune

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Ray Summit is about Ray, the open-source Python framework for building distributed applications that run at any scale. This inaugural, two-day conference showcases Ray best practices, real-world case studies, and the latest research in AI and other scalable systems built on Ray.

Ray Summit

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Ding Ding

Simon Mo

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Qingqing Mao

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How do you bridge the gap between data science and data engineering, which is necessary to reliably and repeatedly create production models and deploy them in production? You’ll hear from experts about various aspects of MLOps. They will discuss the unique challenges of deploying ML to production, along with the tools and techniques necessary for success.

MLOps: Ray in the Real World

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This tutorial introduces you to two of the major Ray libraries: Ray Tune for hyperparameter tuning/optimization (HPO) and Ray Serve for model serving. The Tune tutorial also covers Ray SGD.

Ray Tune and Ray Serve

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Eric Liang

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Anna Luo

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Max Pumperla

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Reinforcement Learning (RL) trains a system to perform a sequence of actions that maximize the rewards in an environment. RL has achieved fame recently as the approach used to beat the world’s best players of games like Go. You’ll hear from RL experts about the current state of the art in RL algorithms and tools, along with real-world applications of RL.

Practical Reinforcement Learning

RLlib is the Ray-based library for implementing reinforcement learning applications. This tutorial will introduce you to RL principles and common algorithms, using a series of example problems.

Reinforcement Learning with Ray RLlib

Richard Liaw

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Liam Li

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Meghana Ravikumar

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The vision of AutoML is to remove as much manual effort and required expertise as possible when applying machine learning and artificial intelligence to real-world problems. This Ray Summit Connect explores two topics of AutoML, where research is ongoing, but many practical tools already exist.

The Road to AutoML: Hyperparameter Tuning and Neural Architecture Search

From the creators of Ray, Anyscale is a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab.

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