Called MobileNets, the pre-trained image recognition models let developers pick between a set of models that vary in size and accuracy to best suit what their application needs.
Right now, a lot of the machine learning inside mobile apps works by passing data off to cloud services for processing and then providing the resulting insights to users once they return over the network. That means it’s possible to use very powerful computers in a data center and alleviate the burden for processing information on a smartphone. The drawback to that approach is that latency and privacy suffer.
By processing data on a user’s smartphone, it’s possible to return results a lot faster, and data never has to leave the phone. However, optimizing a machine learning model for use on mobile is a tall order. Eating up a bunch of battery with computationally intensive machine learning operations is no good.
