982: In Case You Missed It in March 2026
Jon Krohn rounds up March’s interviews in this ICYMI episode. Hear from AI and data science experts across the fields of education and business in this wide-ranging series of clips that take listeners from the Renaissance to the near future. Guests include Lin Quiao (Episode 971), Chris Fregly (Episode 973), Zack Kass (Episode 975), Kyunghyun Cho (Episode 977), and Rohit Choudhary (Episode 979).
Additional materials: www.superdatascience.com/982
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
973: AI Systems Performance Engineering, with Chris Fregly
No one should be manually writing code in 2026, thinks Chris Fregly, Jon Krohn’s guest on this week’s episode. In this interview about Chris’ latest book, AI Systems Performance Engineering, he explains why it’s so important to consider memory bandwidth when evaluating GPU performance, that understanding the full hardware software stack is the most valuable skill for anyone working in AI development, and which shortcuts we still shouldn’t ever take when writing code, even though we might be outsourcing a great deal to generative AI.
This episode is brought to you by the Cisco, by Acceldata and by ODSC, the Open Data Science Conference.
Additional materials: www.superdatascience.com/973
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
In this episode you will learn:
(03:39) Why Chris wrote AI Systems Performance Engineering
(21:39) Essential coding metrics
(37:24) The importance of inference when coding
(42:11) How to manage workflows while using AI agents
(51:37) Where and how to invest in the AI market
Data Science on AWS with Chris Fregly and Antje Barth - #490
Today we continue our coverage of the AWS ML Summit joined by Chris Fregly, a principal developer advocate at AWS, and Antje Barth, a senior developer advocate at AWS.
In our conversation with Chris and Antje, we explore their roles as community builders prior to, and since, joining AWS, as well as their recently released book Data Science on AWS. In the book, Chris and Antje demonstrate how to reduce cost and improve performance while successfully building and deploying data science projects.
We also discuss the release of their new Practical Data Science Specialization on Coursera, managing the complexity that comes with building real-world projects, and some of their favorite sessions from the recent ML Summit.
