Transfer learning
Instead of training from scratch, a model pretrained on abundant data (web text, ImageNet) supplies features or initial weights for a new task, which is then reached by fine-tuning or a small new head. It makes strong models possible with little labeled data.
Example
BERT is pretrained once on unlabeled books and Wikipedia; adding a classification head and fine-tuning on a few thousand labeled reviews yields a strong sentiment classifier.