Schedule: Mobile & Edge sessions

Andrew Selle offers an introduction to TensorFlow Lite and takes you through the conversion, performance, and optimization path while using Android and iOS applications. Read more.

Kaz Sato walks you through AutoML Vision, which allows you to upload labeled images, press a "train" button, and wait for a day to get an image recognition model with state-of-the-art accuracy. Without any ML expertise, you can easily train the model in the cloud, export the TensorFlow Lite model, and use it on mobile devices, Rasberry Pi, and Edge TPU with super low latency and power consumption. Read more.

The future of machine learning is on the edge and on small, embedded devices that can run for a year or more on a single coin-cell battery. Alasdair Allan dives deep into how using deep learning can be very energy efficient and allows you to make sense of sensor data in real time. Read more.

Margaret Maynard-Reid walks you through end-to-end tf.Keras to TFLite to Android, with or without ML Kit. Read more.

There are many cases where developers on mobile write lower-level C++ code for their Android applications using the Android NDK, OpenCV and other technologies. Joe Bowser explores how to use TensorFlow Lite (TF Lite) with an existing C++ code base on Android by using the Android NDK and the TF Lite build tree. Read more.