- Probability math is confusing and difficult, and a base understanding is required to use PPLs in a way that is not true of other ML/DL. Most CS PhDs will not be required to take enough of it to find PPLs intuitive, so to be familiar they will have had to opt into those classes. This is to say nothing of BS/MS practitioners, so the user base is naturally limited to the subset of people who studied Math/Stats is a rigorous way AND opted into the right classes or taught themselves later.
- Probabilistic models are often unique to the application. They require lots of bespoke code, modeling, and understanding. Contrast this with DL, where you throw your data in a blender and receive outputs.
- Uncertainty quantification often is not the most important outcome for sexy ML use cases. That is more frequently things like "accuracy," "residual error," or "wow that picture looks really good".
- PPL package tooling and documentation are often very confusing and don't work similarly to one another. This isn't necessarily the developer's fault, this stuff is hard, and the people with the domain knowledge needed to actually understand this stuff often have spent fewer hours in the open-source trenches.