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

Qingqing Mao headshot

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

Anna Luo headshot

Max Pumperla

Max Pumperla headshot

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

Meghana Ravikumar headshot

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

Ray was created to make it easier to scale diverse computation tasks and distributed state across a cluster, with a minimum of distributed systems expertise and knowledge required. You’ll learn about the particular challenges Ray meets, how Ray works under the hood, how to use Ray in your own applications, and how to debug problems when they occur.

Introduction to Ray: Fast and Simple Distributed Computing

Mike-Jordan

Michael I. Jordan

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Ion Stoica

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Michael Jordan, Pehong Chen Distinguished Professor at UC Berkeley, offers his perspective on the current state and future of ML and AI, in his talk, “The Decision-Making Side of Machine Learning: Computational, Inferential and Economic Perspectives”.

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

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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Anyscale

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