Aish Fenton explains how machine learning is used at Netflix for recommendations but also for many more applications.
Xavier Amatriain discusses how Netflix uses specialized roles, including that of the Data Scientist and Machine Learning Engineer, to deliver valuable data at the right time to Netflix' customer base through a mixture of offline, online, and nearline data processes. Xavier also discusses what it takes to become a Machine Learning Engineer and how to gain real experience in the field.
Max Sklar talks about machine learning at Foursquare, the use of Bayesian Statistics and other methods to build Foursquare's recommendation system and much more.
Ron Bodkin discusses big data architecture, real-time analytics, batch processing, map-reduce, and data science.
Hilary Mason, interviewed by Ryan Slobojan, discuss the engineering behind bit.ly and their use of machine learning in their system architecture. Hilary also talks about their use of MySQL and MongoDB to manage terabytes of information about users and clicks and their implications on performing real-time analysis of anthropology on the human condition.