10 Lessons Learned from building real-life large-scale ML systems

10 Lessons Learned from building real-life large-scale ML systems: 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 the Netflix product and scale to millions of users across many countries. I will discuss issues such as model and feature complexity, sampling, regularization, distributing/parallelizing algorithms, or how to think about offline vs. online computation.

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