Summary
In this concluding chapter, we have aimed to address three questions that may have crossed your mind to bring your existing and future tinyML projects to the next level.
The first question of this chapter centered on the practicality of training a model on microcontrollers. Here, we have ascertained that training is possible, albeit with certain constraints. Nonetheless, despite these limitations, the potential offered by on-device learning is vast, as it enables the creation of intelligent devices capable of learning how to interact with the environment autonomously.
Following that question, we explored the feasibility of deploying generic ML algorithms on microcontrollers, such as random forest, to build even more compact tinyML solutions. In this context, we deployed a trained scikit-learn model on microcontrollers using the emlearn project.
The last question was about powering microcontrollers with batteries. Here, we discussed how to connect batteries in series...
