<edit>I'm also nervous of relying on the recourse of asking package maintainers to edit their variable names to improve compatibility with the random third package I want to use; maybe the culture is different in Julia but in Python that's a good way to get laughed out of the issue tracker :) </edit>
If I were you I would maybe ask people like @systemvoltage who took the plunge and wrote a big project in Julia only to find they had trouble maintaining the project. Maybe one reason he can't upgrade without breaking everything is because of namespace collisions amongst his many dependencies? If not that, it's something like that.
I know in Julia I can just precede every function call and object instantiation with "modulename." and solve the namespace problem that way. What I want to do instead, what I do in python, is bind one namespace of function methods to each object, so that as I code, I don't have to remember which module each each object came from. That is the appeal to me of "model.fit" over "module.fit(model)".
EDIT: This is not some "shave a few seconds off coding time" quality of life issue. This is a mission critical requirements in many enterprise contexts.
Scenario A: Model Development Team trains a model, serializes it, and sends the serialized model object to Production. You want Production to have to lookup which software Package the Model Development Team used for each model, just so they know which "predict" function to call? No, "predict" should just be packaged with the model object as a method, along with "retrain", "diagnose", etc.
Scenario B: I want to fit a dozen different types of models, from multiple packages, on the same data, to evaluate how they each do, and build build some sort of ensemble meta model. So I need to loop over each model object in my complicated ensembling logic. You want me to also programmatically keep track of which module each model comes from?
These are important, happens-every-day use cases in the enterprise.
I think the best solution for this in Julia is to package the model state with all the "method" functions in one struct. Again, this is what MLJ does. This is the closest Julia gets to OOP. But then you either need a lot of boilerplate code, or a Macro to unpack it all for you behind the scenes.
The bigger issue for production in my experience is about packaging the right model with the right version. I don't think anyone has to do `module1.fit` everywhere, since `fit` would've likely come from the same source.
That solves both of your scenarios
I'm a bit confused still though why you say it's a "missing" feature, given that as we discussed above, there is absolutely nothing to stop anyone who wants to use the "Python OOP" style of namespacing in Julia from doing so? Most of us don't seem to find it necessary or to prefer it personally, but that doesn't restrict anyone else from choosing it.
