tinyML Talks: Creating individualized solutions for industrial-grade and environmental problems…

Creating individualized solutions for industrial-grade and environmental problems with TinyML

Kutluhan Aktar
Self-Taught Developer
Edge Impulse Ambassador
Maker
Independent Researcher

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.

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