Deep Learning also works on very small data sets by means of embeddings. A large model trained on large data sets can be used as feature extraction tool for training for small data sets.
For example, consider needing to train hundreds of unique small models every day, based on new customer inputs affecting causality effects for that day (I had to do this for ad forecasting in a past job).
Generating embeddings via pre-trained models essentially produced gibberish and performed far worse than custom feature engineering + simple logistic models.
Of course if all you have are numbers without context, there isn't a lot you can do to improve the situation.