An AI product is not a traditional software product with a model attached. It is a system where every layer has distinct engineering requirements, the quality of output is probabilistic rather than deterministic, and failure modes are different in kind from those in conventional software.
Technical leaders who approach AI products with the mental models of traditional software design will make the right structural decisions for the wrong system. The architecture of an AI product — from data management to serving to evaluation — requires a different set of design principles, and those principles follow from understanding what makes AI systems fundamentally different.