Shreya Shankar: Lessons learned after a year of putting ML into production - Machine Learning Engineered

Episode 9

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,

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01:30 Follow Charlie on Twitter (

02:40 How Shreya got started in CS

06:00 Choosing to concentrate in systems in undergrad (

12:25 Research at Google Brain on fooling humans with adversarial examples (

18:00 Deciding to go into industry instead of pursuing a PhD (

19:35 Why is putting ML into production so hard? (

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 (

01:17:40 Rapid Fire Questions


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

About the Podcast

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Machine Learning Engineered
Helping you bring ML out of the lab and into products that people love.

About your host

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Charlie You

Charlie currently works as a Machine Learning Engineer at Workday. He graduated from RPI with a B.S. in Computer Science and taught himself ML through online courses and projects. In his free time, Charlie enjoys investing and trading, playing poker, and training Brazilian Jiu-Jitsu.