I've been trying to come up with some ML algorithms to model various aspects of basketball and have found some luck simply searching terms of interest in Google Scholar and reading any relevant papers that come up as well as citations.

If I have a large set of input features (1000s+) and most of them can be categorized into one of several feature groups (metadata, feature extractions A, feature extractions B, etc), is it always necessary for your neural network model architecture to reflect your feature groups? For example, is it better to have one flat fully connected layer of all of the features to allow for any type of cross-interactions as opposed to, let's say creating linear or embedding layers for each feature group before combining them together. What are the pros and cons of each? What is usually done in practice?