Summary

Developing an IoT product usually means a lot of integration with third-party systems and platforms. In this last chapter of the book, we had an example of this by developing a connected machine-learning application on ESP32-S3. We downloaded an ML model from the Edge Impulse platform and updated the Edge Impulse SDK for our devkit. On the cloud side, we employed ESP RainMaker. We defined a RainMaker node, device, and parameter in the application to exchange data with the RainMaker platform. The challenge of the project was memory usage. The internal memory of ESP32-S3 was not enough to accommodate all the functionality as specified in the requirements; therefore, we enabled and used SPIRAM to keep the buffers on it. When we look at real-world IoT projects, they also usually need to address such issues, and we had a hands-on experience by working on the project of this chapter.

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