How do I build the whole system around a machine learning model?

Training a model and building the system around it are two different jobs, and being good at one does not make the other simple. The overwhelm you feel is normal. A model is one part. The system is everything that feeds it, serves it, watches it, and catches it when it slips. That is a lot, and no one holds it all at once at first. Think of the model as the engine and the system as the car built around it. The engine alone does not drive anyone anywhere. It needs a way to get data in, a way to send answers out, a way to know when something is wrong, and a way to recover. You do not build all of that in one go. You build one piece, get it working, then add the next. Start with the simplest possible path: get data in, run the model, get a result out, even if it is rough. Make that end to end work before you make any of it good. Once the whole thin line runs, you improve one piece at a time. The overwhelm comes from imagining the finished system. Build the smallest working version first, then grow it.