"I built a deepfake detector. Then I built the tool that proves it actually works."

There's a moment every ML engineer knows and nobody really talks about. You finish training. The validation accuracy looks great. You run model.predict() , it works, and you ship it. Then the quiet questions start. Is it still accurate, or is it slowly getting worse? Has the incoming data drifted away from what it trained on? When the model says it's "92% confident," is it actually right 92% of the time, or is it just loud? Is the input data even valid, or is some silent schema change quietly…