Each month’s Ray Summit Connect includes several short talks on the theme of the event, followed by a panel discussion with audience Q&A.

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Ding Ding

Simon Mo

Simon Mo headshot

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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Eric Liang

Eric Liang headshot

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

Richard Liaw

Richard Liaw headshot

Liam Li

Liam Li headshot

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

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Michael I. Jordan

Michael I. Jordan headshot

Ion Stoica

Ion Stoica headshot

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

Ray Summit Connect

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