Garrett Eardley explores how Riot Games is leveraging Riak for their stats system, discussing why they chose Riak, the data model and indexes, and strategies for working with eventually consistent data.
Ronny Kohavi shares lessons learned, cultural and scaling challenges conducting hundreds of concurrent online controlled experiments at Bing.
Jonathan Seidman and Ramesh Venkataramaiah present how they run R on Hadoop in order to perform distributed analysis on large data sets, including some alternatives to their solution.
Nathan Marz explain Storm, a distributed fault-tolerant and real-time computational system currently used by Twitter to keep statistics on user clicks for every URL and domain.
Hilary Mason presents the history of machine learning covering some of the most significant developments taking place over the last two decades, especially the fundamental math and algorithmic tools employed. She also exemplifies how machine learning is used by bit.ly to discover various statistical information about users.