If you want to deploy your TensorFlow model to a mobile or embedded device, a large model may take too long to download and use too much RAM and CPU, all of which will make your app unresponsive, heat the device and drain its battery. To avoid this, you need to make a mobile-friendly. Lightweight, and efficient model, without sacrificing too much of its accuracy.

Before Deploying a TensorFlow model to a mobile, I suggest you to learn how to Deploy a machine learning model to a Web Application. This will help to to understand things better before getting into to deploy a TensorFlow model to a Mobile or embedded Device.

The file library provides several tools to help you deploy your TensorFlow model to a mobile and embedded devices, with three main objectives:

• Reduce the model size to shorten download time and reduce RAM usage.
• Reduce the number of computations needed for each prediction to minimize latency, battery usage, and heating.
• Adapt the model to device-specific constraints.

Train and Deploy a TensorFlow Model to a Mobile

While you Deploy a Machine Learning Model, you need to reduce the model size, TFLite’s model converter can take a saved model and compress it to a much lighter format based on FlatBuffers. This is a dynamic, cross-platform serialization library initially created by Google without any preprocessing: this reduces the loading time and memory footprint.

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