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Getting machine learning models ready for use on-device is a major challenge. Drag-and-drop training tools can get you started, but the models they produce aren’t small enough or fast enough to ship. In this talk, you’ll learn optimization, pruning, and compression techniques that keep app sizes small and inference speeds high. You’ll start by discovering flexible model architectures that meet performance and accuracy requirements across devices and platforms. You’ll then learn pruning and distillation techniques to optimize model performance and use quantization tools to compress models to a fraction of their original size. Finally, you’ll explore a practical example of this process as you create an artistic style transfer model that’s just 17kb. All of these techniques will be applied to mobile machine learning frameworks for Android, i.e. TensorFlow Lite.

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