Making AI models work for real-world applications | Aarti Bagul, ML Engineer @Snorkel AI logo

Making AI models work for real-world applications | Aarti Bagul, ML Engineer @Snorkel AI

Mar 24, 2021
Aarti is a machine learning engineer at Snorkel AI. Prior to that, she worked closely with Andrew Ng in various capacities - at AI Fund helping build ML companies from scratch internally, as well as investing in ML companies, as a machine learning engineer at his startup Landing AI, as head TA for his deep learning class at @Stanford (CS230), and in his research lab at Stanford. She graduated with a master’s in CS from Stanford, and with bachelors in CS and Computer Engineering from @New York Universitywhere she worked in David Sontag’s lab on applications of machine learning to clinical medicine, and at @Microsoft Research as a research intern for John Langford, where she contributed to Vowpal Wabbit, an open-source project. About the Host: Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

00:00 Introductions 01:40 Can you tell us a bit about your path into Machine Learning? What built your interest in ML? 04:02 Tell us about your role as ML engineer at Snorkel AI 12:10 What kind of challenges have noticed while working with ML applications in production, that most newbies don't know as students or fresh graduates? 17:50 ML applications production pipeline 23:12 Tell us a bit about your role at AI Fund? 28:55 What were few of the surprising yet interesting things you learned while working within hybrid roles there; jumping from being a PM to ML engg to VC roles? 32:15 Where do you think companies/startups fail at taking models from research to production? 37:35 what do you think is the best way for people to learn the basics of ML and at the same time learn more about building scalable and reliable systems out of it? 40:52 What excites you more: pure ML research or something like an application oriented like at AI Fund or on DevOps angle at SnorkelAI? 44:25 What is one underrated and one overrated aspect of AI powered applications that startups/companies are building that most people without an ML background get it wrong? 51:20 What is one research topic/area that you are really interested and optimistic about in the near future, even though you might not be directly working on it?