In October 2006, the DVD rental company Netflix released more than 100 million user ratings of movies for a competition to predict users’ ratings based on prior ratings.  One allure to data analysts around the world was a $1,000,000 prize for a team achieving a ten percent reduction in root mean squared prediction error relative to Netflix’s current algorithm.  The size of the data (over 17,000 movies and 480,000 users) and the nature of human-movie interactions produced many modeling challenges.  After describing some of the techniques in use and advances spurred by the competition, I will offer lessons and raise some questions about building massive prediction models, the role of statistics versus computer science in such endeavors, and prizes as a way to advance science.  This is joint work with Chris Volinsky and Yehuda Koren, current and former colleagues at AT&T Labs-Research.