21. Adam Waksman - Data science is becoming software engineering
Towards Data Science
02/16/20
•44m
About
Comments
Featured In
When I think of the trends I’ve seen in data science over the last few years, perhaps the most significant and hardest to ignore has been the increased focus on deployment and productionization of models. Not all companies need models deployed to production, of course but at those that do, there’s increasing pressure on data science teams to deliver software engineering along with machine learning solutions.
That’s why I wanted to sit down with Adam Waksman, Head of Core Technology at Foursquare. Foursquare is a company built on data and machine learning: they were one of the first fully scaled social media-powered recommendation services that gained real traction, and now help over 50 million people find restaurants and services in countries around the world.
Our conversation covered a lot of ground, from the interaction between software engineering and data science, to what he looks for in new hires, to the future of the field as a whole.
Previous Episode
20. Chanchal Chatterjee - Real Talk with AI Leader at Google
January 30, 2020
•38m
In this podcast interview, YK (CS Dojo) interviews Chanchal Chatterjee, who’s an AI leader at Google.
Next Episode
22. Luke Marsden - Data Science Infrastructure and MLOps
February 23, 2020
•40m
You train your model. You check its performance with a validation set. You tweak its hyperparameters, engineer some features and repeat. Finally, you try it out on a test set, and it works great!
Problem solved? Well, probably not.
Five years ago, your job as a data scientist might have ended here, but increasingly, the data science life cycle is expanding to include the steps after basic testing. This shouldn’t come as a surprise: now that machine learning models are being used for life-or-death and mission-critical applications, there’s growing pressure on data scientists and machine learning engineers to ensure that effects like feature drift are addressed reliably, that data science experiments are replicable, and that data infrastructure is reliable.
This episode’s guest is Luke Marsden, and he’s made these problems the focus of this work. Luke is the founder and CEO of Dotscience, a data infrastructure startup that’s creating a git-like tool for data science version control. Luke has spent most of his professional life working on infrastructure problems at scale, and has a lot to say about the direction data science and MLOps are heading in.
If you like this episode you’ll love
Promoted