Lessons Learned from Building Real-Life ML Systems

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Xavier Amatriain, VP of Engineering, Quora

Xavier Amatriain (http://xavier.amatriain.net/) is VP of Engineering at Quora, where he leads the team building the best source of knowledge in the Internet. With over 50 publications in different fields, Xavier is best known for his work on Machine Learning in general, and Recommender Systems in particular. Before Quora, he was Research/Engineering Director at Netflix, where he lead the team building the famous Netflix Recommendation algorithms. Previously, Xavier was also Research Scientist at Telefonica Research Barcelona and Research Director at University of California Santa Barbara. He has also lectured at different universities both in the US and Spain and is frequently invited as a speaker at conferences and companies. Although he currently lives in the heart of Silicon Valley, he is a proud Catalan and Barcelonian.


There are many good textbooks and courses where you can be introduced to machine learning and maybe even learn some of the most intricate details about a particular approach or algorithm. While understanding that theory is a very important base and starting point, there are many other practical issues related to building real-life ML systems that you don’t usually hear about. In this talk I will share some of the most important lessons learned in years of building the large-scale ML solutions that power products like Netflix or Quora and scale to millions of users across many countries. I will discuss issues such as model and feature complexity, sampling, regularization, distributing/parallelizing algorithms, the importance of metrics, or how to think about offline vs. online computation. I will also describe how to combine supervised and non-supervised approaches or the role of ensembles in practical ML systems.

This will be a remote session. Languange: english.

We would like to thank the Unitat d'Excel·lència Maria de Maeztu DTIC-UPF for their support on the organization of the event.