A research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
Transfer Learning lets organizations take a large open-source model trained on broad public data and apply its general language capability to specialized, proprietary enterprise data, which saves compute while still reaching high accuracy.
See also Large Language Model, Fine-Tuning and Model Inference.