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Created page with "{{InfoBoxTop}} {{AppliesToMachineLearning}} {{AppliesTo Machine Learning TN}} {{InfoBoxBottom}} __FORCETOC__ == History == {| class="wikitable" border="1" !Version !Date !No..."
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== History ==
{| class="wikitable" border="1"
!Version
!Date
!Notes
|-
|1.0.0
|March 2021
|First public release
|}
==Introduction==
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 can be addressed with Machine Learning (ML) algorithms effectively: defect detection by automatic visual inspection. More specifically, this focuses deals with the inspection of assembled Printed Circuit Boards (PCBs). The aim is to determine if it would be possible to design innovative machines exploiting ML algorithms and able to ouperform traditional devices employed for this task.
{{AppliesToMachineLearning}}
{{AppliesTo Machine Learning TN}}
{{InfoBoxBottom}}
__FORCETOC__
== History ==
{| class="wikitable" border="1"
!Version
!Date
!Notes
|-
|1.0.0
|March 2021
|First public release
|}
==Introduction==
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 can be addressed with Machine Learning (ML) algorithms effectively: defect detection by automatic visual inspection. More specifically, this focuses deals with the inspection of assembled Printed Circuit Boards (PCBs). The aim is to determine if it would be possible to design innovative machines exploiting ML algorithms and able to ouperform traditional devices employed for this task.
