When not to deploy a model

There is a quiet skill in machine learning that rarely makes the demos: knowing when to stop. A model can pass its evaluation, impress in a notebook, and still be the wrong thing to put in front of real people. Recognizing that moment is part of the craft.

Start with the cost of being wrong. A model that recommends a song can be sloppy and still be fine. A model that influences a security decision or a person's opportunity cannot. The higher the stakes, the higher the bar, and sometimes the bar is simply out of reach for now.

Then look at the data honestly. If the data the model learned from does not resemble the world it will meet, its confidence is misplaced. A clean test score on the wrong distribution is a comforting illusion.

Holding a model back is not failure. It is judgment. We teach people to build models well, and we teach them to say not yet when that is the right answer.