Ray's co-creator Robert Nishihara on the state of distributed computing in Machine Learning
11.13.2020 - By Gradient Dissent - A Machine Learning Podcast by W&B
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.
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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