The last few years have seen deep learning make significant advances in fields as diverse as speech recognition, image understanding, natural language understanding, translation, robotics, and healthcare. In this talk I'll describe some of the machine learning research done by the Google Brain team (often in collaboration with others at Google). As part of our research, we have built two systems, DistBelief, and TensorFlow, for training large-scale deep learning models on large datasets. I'll describe some of the distributed system techniques we use to scale training of such modelsbeyond single devices, as well describe some of the design decisions and implementation of TensorFlow system, which was open sourced in November, 2015.