Summary

The recipes presented in this chapter demonstrated how to deploy a model trained with TensorFlow using tflite-micro on Arduino-compatible platforms, such as the Arduino Nano and Raspberry Pi Pico.

Initially, we learned how to build a dataset to forecast snow using the temperature and humidity over the last three hours. In this part, we focused on the importance of feature scaling and proposed the Z-score function to bring the input features to a similar numerical range.

Afterward, we delved into the model training phase. Here, we trained the network with TensorFlow and learned how to make the model compact with TensorFlow Lite using 8-bit quantization.

Then, we discovered how to acquire temperature and humidity measurements with the Arduino Nano and Raspberry Pi Pico. In particular, we learned how to use the built-in sensor on the Arduino Nano and connect the DHT22 sensor to the Raspberry Pi Pico.

Finally, we deployed the trained model on the microcontrollers...