When using artificial intelligence (AI) as part of a quality inspection system, many programs require a data expert to help train the model.
However, that person is not the most knowledgeable about the product itself.
In this article, Zohar Kantor, Chief Revenue and Customer Success Officer at artificial intelligence developer Qualisense assesses whether the data experts or quality managers are the best candidates for helping train the model.
Artificial intelligence has the potential to deliver game-changing results for quality inspection and defect detection. However, until now the process of training the model has proven problematic. The traditional, supervised model requires extensive input from the quality manager and other personnel involved in the production process. For many, the hassle and time this process takes means it is not a worthwhile investment.
The alternative, unsupervised model promises much more on paper, but often fails to deliver in practice. For an unsupervised model, minimal input is required to get the model up and running, but you need input at a later stage to optimise the model.
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