  Efficient automated detection of flux-transient, reoccurring flux-variable,
and moving objects is increasingly important for large-scale astronomical
surveys. We present braai, a convolutional-neural-network, deep-learning
real/bogus classifier designed to separate genuine astrophysical events and
objects from false positive, or bogus, detections in the data of the Zwicky
Transient Facility (ZTF), a new robotic time-domain survey currently in
operation at the Palomar Observatory in California, USA. Braai demonstrates a
state-of-the-art performance as quantified by its low false negative and false
positive rates. We describe the open-source software tools used internally at
Caltech to archive and access ZTF's alerts and light curves (Kowalski), and to
label the data (Zwickyverse). We also report the initial results of the
classifier deployment on the Edge Tensor Processing Units (TPUs) that show
comparable performance in terms of accuracy, but in a much more (cost-)
efficient manner, which has significant implications for current and future
surveys.
