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Published on Jun 27, 2014
Large scale machine learning is playing an increasingly important role in improving the quality and monetization of Internet properties. A small number of techniques, such as regression, have proven to be widely applicable across Internet properties and applications. Sibyl is a research project that implements these primitives at scale and is widely used within Google. In this talk I will outline Sibyl and the requirements that it places on Google's computing infrastructure.
Tushar Chandra is a Principal Engineer at Google Research and a co-lead for the Sibyl project. He received his Ph.D. in Computer Science from Cornell University in 1993, worked at IBM Research thereafter until he joined Google in 2004. He has worked on a number of distributed systems projects: Reliable Scalable Cluster Technology, Gryphon, and Oceano at IBM and Bigtable and a Paxos-based platform for fault-tolerance at Google. He was a joint winner of the 2010 Edsger W. Dijkstra Prize in Distributed Computing.
Introduced by Georgia Tech professor and DSN 2014 General Chair, Dr. Doug Blough, this keynote was presented at the IEEE DSN (Dependable Systems and Networks) conference at the Georgia Tech Hotel & Conference Center in Atlanta, GA on Wednesday, June 25th 2014.