Tranfer learning is a form of this, where you train the first half of a neural net over a huge set of data, then take a much more specific task and train just a bit more by adding some layers, and you get great sometimes industry leading results. Look up Google's BERT project, trained generically on English texts and then you can reuse that part in many different tasks like sentiment analysis or entity recognition or translation. An amazing way to leverage Googles compute and data access and do cool things even as a single developer.