Teams expect too much from AI applications at launch and fail to build the infrastructure to keep improving them.

I think, in ML and AI engineering, that teams often expect too high of accuracy or alignment with their expectations from an AI application right after it’s launched, and often don’t build out the infrastructure to continually inspect data, incorporate new tests, and improve the end-to-end system. Then, over time, they only get more and more disappointed.