The story of Ray and what lead Robert to go from reinforcement learning researcher to creating open-source tools for machine learning and beyond

Robert is currently working on Ray, a high-performance distributed execution framework for AI applications. He studied mathematics at Harvard. He’s broadly interested in applied math, machine learning, and optimization, and was a member of the Statistical AI Lab, the AMPLab/RISELab, and the Berkeley AI Research Lab at UC Berkeley.

robertnishihara.com

https://anyscale.com/

https://github.com/ray-project/ray

https://twitter.com/robertnishihara

https://www.linkedin.com/in/robert-nishihara-b6465444/

Topics covered:

0:00 sneak peak + intro

1:09 what is Ray?

3:07 Spark and Ray

5:48 reinforcement learning

8:15 non-ml use cases of ray

10:00 RL in the real world and and common uses of Ray

13:49 Ppython in ML

16:38 from grad school to ML tools company

20:40 pulling product requirements in surprising directions

23:25 how to manage a large open source community

27:05 Ray Tune

29:35 where do you see bottlenecks in production?

31:39 An underrated aspect of Machine Learning

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