The Variational Book // Yuri Plotkin // MLOps Podcast
The Variational Book // MLOps Podcast #253 with Yuri Plotkin, an ML Scientist.
A huge thank you to @SAS for their generous support!
// Abstract
Curiosity has been the underlying thread in Yuri's life and interests. With the explosion of Generative AI, Yuri was fascinated by the topic and decided he needed to learn more. Yuri pursued learning by reading, deriving, and understanding seminal papers within the last generation. The endeavors culminated in the writing of a book on the topic, The Variational Book, which Yuri expects to release shortly in the coming months. A bit of detail about the topics he covers can be found here: www.thevariationalbook.com.
// Bio
Evolved from biomedical engineer to wet-lab scientist, and more recently transitioned Yuri's career to computer science with the last 10+ years developing projects at the intersection of medicine, life sciences, and machine learning.
Yuri's educational background is in Biomedical Engineering, at Columbia University (M.S.) and the University of California, San Diego (B.S.). Current interests include generative AI, diffusion models, and LLMs.
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// Related Links
Website: https://plotkiny.github.io/
The Variational Book: www.thevariationalbook.com
SAS: https://www.sas.com/en_us/home.html
SAS® Decision Builder: https://www.sas.com/en_us/offers/23q4...
Data Engineering for AI/ML Conference: https://home.mlops.community/home/eve...
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Timestamps:
[00:00] Yuri's preferred coffee
[00:23] Takeaways
[01:56] Register for the Data Engineering for AIML Conference now!
[02:47] Yuri's background
[06:13] The Variational Book
[10:25] Not including LLMs in the book
[12:14] Diffusion models
[16:37] Evolution within diffusion models
[20:55] Diffusion models for video
[25:43] Evolution and optimization of algorithms
[28:53] Markovian
[33:06 - 34:52] SAS Ad
[34:53] Understanding Markovian vs Non-Markovian models
[40:28] Visualizing model evolution
[43:46] Models through time
[44:53] The Variational Book inspiration
[47:53] Influencing LLM latent space
[51:07] Understanding ML Architectures
[52:56] Balancing AI complexity
[55:05] Wrap up