Finding the best machine learning model for analyzing sensor data isn't easy. What pre-processing steps yield the best results, for example? And what signal processing parameters should you pick? The selection process is even more challenging when the resulting model needs to run on a microcontroller with significant latency, memory and power constraints. AutoML tools can help, but typically only look at the neural network, disregarding the important roles that pre-processing and signal processing play with tinyML.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/how-to-optimize-ml-model-accuracy-in-resource-constrained-embedded-applications