This paper presents a systems-oriented approach to embedded machine learning on microcontroller-class edge devices, focusing on the engineering decisions involved in deploying machine learning models on resource-constrained platforms. The workflow covers data acquisition, preprocessing, feature extraction, model deployment, and inference. The authors provide a detailed evaluation of the design choices and their impact on memory, energy, and latency. You can use this approach to optimize your own