In the traditional software world, release means done. In AI, release means the learning has just begun. A machine learning model might perform well in the lab, but when exposed to real users, dynamic inputs, and edge cases, things change. Accuracy fluctuates. Expectations shift. New data flows in. And that’s when the most important part of AI development begins: post-deployment...