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Shreya Shankar: Lessons learned after a year of putting ML into production

Podcast |: Machine Learning Engineered
Publisher |: Charlie You
Media Type |: audio
Podknife tags |: Artificial Intelligence,; Business,; Careers,; Data Science,; Interview,; Machine Learning,; Technology
Categories Via RSS |: Business,; Careers,; Science,; Technology
Publication Date |: Oct 20, 2020
Episode Duration |: 01:24:00

Shreya Shankar is a Machine Learning Engineer at Viaduct AI. She's a master's student at Stanford and has previously worked at Facebook and Google Brain. She writes some truly excellent articles about machine learning on her personal blog, shankar.com/">https://www.shreya-shankar.com/

Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: http://bit.ly/mle-newsletter

Comments? Questions? Submit them here: http://bit.ly/mle-survey

Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Subscribe to ML Engineered: https://mlengineered.com/listen

Timestamps:

01:30 Follow Charlie on Twitter (http://twitter.com/charlieyouai)

02:40 How Shreya got started in CS

06:00 Choosing to concentrate in systems in undergrad (shankar.com/systems/">https://www.shreya-shankar.com/systems/)

12:25 Research at Google Brain on fooling humans with adversarial examples (adversarial-examples-that-fool-both-computer-vision-and-time-limited-humans.pdf">http://papers.nips.cc/paper/7647-adversarial-examples-that-fool-both-computer-vision-and-time-limited-humans.pdf)

18:00 Deciding to go into industry instead of pursuing a PhD (shankar.com/new-grad-advice/">https://www.shreya-shankar.com/new-grad-advice/)

19:35 Why is putting ML into production so hard? (shankar.com/making-ml-work/">https://www.shreya-shankar.com/making-ml-work/)

25:00 Best of the research graveyard

29:05 Checklist for building an ML model for production

34:10 Ensuring reproducibility

39:25 Back to the checklist

44:25 PM for ML engineering

48:50 Monitoring ML deployments

53:50 Fighting ML bias

58:45 Feature engineering best practices

01:02:30 Remote collaboration on data science projects

01:07:45 AI Saviorism (shankar.com/ai-saviorism/">https://www.shreya-shankar.com/ai-saviorism/)

01:17:40 Rapid Fire Questions

Links:

shankar.com/systems/">Why you should major in systems

adversarial-examples-that-fool-both-computer-vision-and-time-limited-humans.pdf">Adversarial Examples that Fool Both Computer Vision and Time-Limited Humans

shankar.com/new-grad-advice/">Choosing between a PhD and industry for new computer science graduates

shankar.com/making-ml-work/">Reflecting on a year of making machine learning actually useful

shankar.com/ai-saviorism/">Get rid of AI Saviorism

Shreya discusses what she's learned in the past year about making ML useful by putting into production. She touches on strategies for ensuring reproducibility, feature engineering best practices, and her checklist for building AI-driven systems.