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In [[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1|this series]] of articles, different embedded platforms suited for building "[[https://towardsdatascience.com/will-edge-ai-be-the-ml-architecture-of-the-future-42663d3cbb5 |"Edge AI"]]" solutions are compared from the point of view of inferencing capabilities/features, development tools, etc. In principle, such platforms can drive a bunch of different applications in the industrial world and other fields as well.

This series of Technical Notes illustrates a feasibility study regarding a common problem in the manufacturing realm that supposedly Machine Learning (ML) algorithms can address effectively: defect detection by automatic visual inspection. More specifically, this study deals with the inspection of assembled Printed Circuit Boards (PCBs). The ultimate goal is to determine if it would be possible to design innovative machines exploiting ML algorithms and able to outperform traditional devices employed today employed for this task. This is a blatant example of AI at the edge as the application requirements include the following: