My group is working on a ML model that can work with little data (and bad accuracy) as long as there actually is little data available but can easily be extended as soon as said data becomes available (Think of interpolating existing models and then creating an individual predictor). This is due to a business requirement of the relevant application (new plants get installed over time but need to be integrated seamlessly). Transforming from a coarse yet cheap prediction to an accurate but expensive prediction takes labor effort that the company can invest as desired.
Are there research areas that take into account such "evolutionary transformation processes" which enable a tradeoff between costs and accuracy? What are the right keywords to look for?
I am looking for keywords/papers/communities.