Tools for scaling machine learning

The Data Exchange Podcast: Paco Nathan, Jenn Webb, and Ben Lorica on Ray as a framework for building frameworks, and the past, present, and future of the Data Exchange Podcast.


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In this episode of the Data Exchange, our special correspondent and editor Jenn Webb organized a mini-panel composed of myself and Paco Nathan, author, teacher, and founder of Derwen.ai, a boutique consulting firm specializing in Data, machine learning (ML), and AI.


[A video version of this episode is available on our YouTube Channel.]


We began by discussing tools for scaling machine learning. Paco and I have been impressed with the growth in the number of libraries being built on top of Ray as well as the variety of use cases that are being addressed by Ray.  As I noted in a recent post:

“Ray is both a general-purpose distributed computing platform and a collection of libraries targeted at machine learning and other workloads. Think of Ray as both a general-purpose distributed computing platform and as a collection of libraries targeted at machine learning and other workloads. Unlike monolithic platforms, users of Ray are free to use one or more of the existing libraries, or to use Ray to build their own.”

We then discussed the upcoming Ray Summit, a FREE virtual conference featuring over 50 talks on machine learning, Python, serverless and cloud native technologies.

 

We also looked back at the first eight months of this podcast (here’s an archive of previous episodes).  Both Paco and Jenn were instrumental in getting this podcast started, and I wanted to mark crossing the thirty episode threshold with a short retrospective.

 

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[Image: Ras Al Khaimah sand dune by Ben Lorica.]