Comment by perrygeo
The worst offender is "feature". In my domain (ML and geo) we have three definitions.
Feature could be referring to some addition to the user-facing product, a raster input to machine learning, or a vector entity in GeoJSON. Context is the only tool we have to make the distinction, it gets really confusing when you're working on features that involve querying the features with features.
You can say the same thing about “model” even in ML. Depending on the context it can be quite confusing:
1) an architecture described in a paper
2) the trained weights of a specific instantiation of architecture
3) a chunk of code/neural net that accomplishes a task, agnostic to the above definitions