Model Training & Adaptation
Domain adaptation
Adjusting a model so it performs better on data from a specific field or environment.
Example
A model is adjusted on medical language so it performs better in a clinical setting.
Why it matters
It helps a system work in environments unlike the ones it first learned from.
You'll hear it
“Our customers use these words differently from the original examples.”
Can you control it?
Set by the team
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Is changing the label on the product enough?
- No. The system needs suitable adaptation or evidence that it already handles the new setting.
- Can success in one setting hide failure in another?
- Yes. Familiar-looking tasks can differ in important details or assumptions.
- Does adapting to a new field guarantee expert reliability?
- No. Specialized mistakes can remain and need appropriate checking.