In the last decade, Facebook has quickly risen to become the most popular social networking and communication site in the world, with over 750 million daily active users. In this talk, I describe the advertising systems that we have developed to support and complement that network. With over one million active advertisers, in order to select what ads each person sees, Facebook’s ad ranking system evaluates many trillion candidate matches every day. I will share a few practical lessons gathered from working on machine learning at that scale and cover some of the broad learnings and improvements that we have made. I will also relate this to the application of machine learning in online advertising overall, and suggest that understating what separates the current systems and approaches from the real world is more important than striving to achieve perfection.