Model drift is the decay in a model's real-world performance after it ships. The weights never change. The world those weights were fitted to does change, so yesterday's good fit becomes today's mediocre one.
Three things move underneath a frozen model. User behaviour shifts with seasons, prices, and competitors. Upstream systems change, so a field that meant one thing now means another. The model also changes behaviour by acting on it, since users mostly see what it ranked highly.
So a launch-day accuracy of 92 percent is not a property of the model. It is a measurement of one moment. Treating it as permanent is how a fraud model quietly misses a scam pattern invented after training ended. Plan for decay from day one, and budget retraining as a running cost rather than a rescue project.
This answer doesn't lend itself to a diagram - it reads best . No credits were charged.
Why there's no diagram: “”
The interactive diagram is below the answer - jump to diagram ↓ · Below it, the related concept . Jump to it ↓
The diagram below the answer is the concept . Jump to it ↓