AI engineering glossary · ML fundamentals
What is distribution shift?
Distribution shift (also called Dataset shift): A change between the data a model was trained on and the data it faces later, so its learned patterns stop matching reality. Shifts can come from changing customers, seasons, sensors or policies, and they quietly degrade performance unless monitored.
Distribution shift: an example
A demand model trained before a festival season sees very different buying patterns during it and loses accuracy.
Related terms
More ML fundamentals terms
- Double descent
- Early stopping
- Empirical risk minimisation
- Feature
- Feature space
- Generalisation
- Generalisation error
- Generalisation gap
Where you learn it
Distribution shift is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.