How can we deploy scikit-learn models on microcontrollers?
Artificial neural networks are incredibly accurate and versatile to solve a wide range of data analysis problems. However, this model is not the only player in the ML arena. Indeed, many other ML models are available that can be just as effective for specific tasks and be less compute- and memory-demanding.
In this recipe, we will learn how to deploy a random forest model trained with the scikit-learn framework on the Arduino Nano and Raspberry Pi Pico with the Python emlearn project.
Getting ready
Whether you are just starting in ML, an enthusiast, or a researcher, you will have probably come across the scikit-learn (https://scikit-learn.org/) framework, a pillar, like TensorFlow, of the ML community.
As we have seen through all the projects developed in this book, TensorFlow is a low-level library providing the building blocks for ML algorithms, particularly for creating deep learning models. Scikit-learn...
