Operationalizing ML is hard. But it has nothing to do with the models at all. It is hard because the main use-case (besides Images and Text processing) is feature fusion, you generate a bunch of distinct features about say People and their history and the products they like etc (thinking of a Recommender-System now). However, these are things that usually live in really distinct parts of your DB / your backend. So as ML ops you are now tasked with getting info from all of these places. In a big org, often with different responsible people, security protocols etc....
Data acquisition is definitely hard, but it's far from the only challenge. Labeling is also hard for many use cases. Curating your labels is pretty annoying. Making your model inference performant enough to be launchable is also hard. Making your model something you can quickly iterate on is also hard. Evaluating your model (with the system it's embedding in) is also hard.
I wouldn't say it has "nothing" to do with the models. Maybe "little to do with model architecture" and "a lot to do with everything around the model." There's just a lot of work to be done to get the business wins you want from ML.
