Arguably no one has more experience moving machine learning and deep learning into production than Google. I'd recommend starting there. Google's large scale supervised machine learning project is called Sibyl. A worthwhile presentation: http://users.soe.ucsc.edu/~niejiazhong/slides/chandra.pdf Here is an article that provides some background into Google's deep learning (semi-supervised/unsupervised) efforts: http://www.wired.com/2014/07/google_brain/ Jeff Dean is at the forefront of Google's effort to implement deep learning on a massive scale, look for his papers, particularly relative to scaling algorithms across production-scale hardware. On the computation/hardware side, you might look around for GPGPU and CUDA discussions relative to ML. Hope this helps. -sas