So vectors to vectors are extremely abstract and it's hard to understand what those vectors are. ML/DL are functions at the end of the day that are learned from observing a lot of examples of vectors -> vector transformations (inductive learning).

If these transformations are fairly easy to model, you might just be able to solve the function directly. Or you might need simple ML algorithms. If it's very complex, you might need stronger models and/or more data.

For instance image -> vector of probabilities over possible categories requires some powerful models where as a vector of 1 feature (height) -> probability of not hitting the head on the roof requires a basic model.