Format of data in SQL for machine learning
Disclaimer: I'm not at all expert about deploying big ML systems in production. This answer is only based on my experience with many different ML problems and datasets

My humble advice would be not to try to design the format of the data before having a quite precise idea of what kind of ML process is going to be applied. There is no "one size fits all" in ML and there's a real risk that by starting with the format of the data, you will end up with something which turns out to be completely inappropriate for the task.

Start with local experiments instead:


Use a small subset of the data at first.
Design some simple problems of the same kind as the real ones you plan to do eventually. 
Vary as many aspects as possible of the experiments: preprocessing, learning algorithms, parameters, size of the data, etc. Move progressively to more realistic tasks and amount of data.
Evaluate the advantages/disadvantage of the different methods/setups, then select a range of target setups
Finally design everything including the data format based on these target setups.


Following this logic during the experimental stage you can just export your data in any format convenient for whatever framework you're testing. it's only at the end of the experimental stage that you design the production system, e.g. SQL server etc.