The Most Common ML Quality Engineering Mistakes
20 min readSep 30, 2022
Engineering quality in AI systems today — or even understanding what quality means — is still an art, and an art which few companies, practitioners and researchers seem to be able to master. In many ways, the distance between the hopes and the practices of AI providers and the actual outcomes for customers is massive, as it was for the early days of software engineering when software started to be something that serves enterprise customers and consumers rather than being a tool for scientists.
