“Garbage in, garbage out” serves as a simple reminder that successful machine vision systems must start with quality data. Current challenges require flexible machine vision systems and models that can keep up with the speed of technology. Manufacturers have found value in partnering deep learning with machine vision to offer dynamic solutions that can compensate for lower-quality inputs. Despite deep learning compensation, however, starting with quality inputs will yield better results. The following explains the basics of rule-based and learning-based machine vision. Additionally, a case study presents evidence against the myth that learning-based solutions can compensate for lower-quality inputs in machine vision.