Loading Episode...
Machine Learning Engineered - Charlie You EPISODE 9, 20th October 2020
Shreya Shankar: Lessons learned after a year of putting ML into production
00:00:00 01:24:00

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

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, 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 (https://www.shreya-shankar.com/systems/)

12:25 Research at Google Brain on fooling humans with adversarial examples (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 (https://www.shreya-shankar.com/new-grad-advice/)

19:35 Why is putting ML into production so hard? (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 (https://www.shreya-shankar.com/ai-saviorism/)

01:17:40 Rapid Fire Questions


Links:

Why you should major in systems

Adversarial Examples that Fool Both Computer Vision and Time-Limited Humans

Choosing between a PhD and industry for new computer science graduates

Reflecting on a year of making machine learning actually useful

Get rid of AI Saviorism

Designing Data Intensive Applications