Facebook is powered by machine learning and AI. From advertising relevance, news feed and search ranking to computer vision, face recognition, and speech recognition, we run ML models at massive scale, computing trillions of predictions every day. I'll talk about some of the tools and tricks we use for scaling both the training and deployment of some of our deep learning models at Facebook. I'll also cover some useful libraries that we've open-sourced for production-oriented deep learning applications.
I'm a research engineer at Facebook, working on the Facebook AI Research and Applied Machine Learning teams to drive the large amount of AI applications at Facebook. At Facebook, I've worked on the large scale event prediction models powering ads and News Feed ranking, the computer vision models powering image understanding, and many other machine learning projects. I'm a contributor to several deep learning frameworks, including Torch and Caffe. Before Facebook, I obtained a masters in mathematics from the University of Cambridge, and a bachelors in mathematics from the University of Sydney.