You can look around online but I don’t think that is really true.
You can check the videos from the developer conference here 21 for example. Or other blogpost about how to use pytorch in production.
To answer your design problem as I understand it, I would say that as soon as you start working with tera/peta bytes of data for your dataset. You are working in a private company that has its own infrastructure to support such data. And so PyTorch cannot provide a fast and reliable way to load data on an infrastructure that we don’t know about.
If you only have small datasets or few hundred gigabytes of data. You most likely don’t want to use cloud storage for your training as it will be slowing down your training significantly (even a spinning disk will most likely be the bottleneck for most network).
Did I understood your problem correctly?