TensorFlow Mobile makes it possible for developers to incorporate a wide range of comprehension and classification techniques into their iOS or Android applications. This is particularly useful given the range of sensor data available on mobile phones. Pretrained TensorFlow models can be loaded into a mobile application and applied to inputs such as live video frames, text or speech. Mobile phones present a surprisingly opportune platform for implementing these computational models. TensorFlow models are exported and loaded as protobuf files, which can present some problems for implementers. Protobuf's binary format can make it hard to examine models and requires that you link the correct protobuf library version to your mobile app. But local model execution offers an attractive alternative to TensorFlow Serving without the communication overhead of remote execution.
