In light of recent developments in artificial intelligence and machine learning, it has become more tempting to apply machine learning to solve industrial-grade, environmental, and health-related issues. Nevertheless, at least for now, devising a solution for a large-scale problem with ML can lead to interminable workloads and exorbitant costs depending on the nature of the targeted problem. For instance, building a ubiquitous pest detection system with object recognition requires an abundance of miscellaneous data sets due to differing subspecies, soil types, environmental factors, etc.

Instead of focusing on solving a problem in every possible scenario with ML, we can create individualized solutions for industrial-grade and environmental issues with considerably low budgets and workloads. Like individualized treatment plans, the accumulation of tailored and refined ML solutions for large-scale problems instigates a significant surge in revolutionizing our world. In this presentation, to fortify the concept of benefiting from TinyML and edge devices to create individualized solutions for large-scale problems, I will demonstrate some of my proof-of-concept AIoT projects.