LudicrousParrot69
I "think" I have a better handle on what you wish to do.
Is it kind of generic "serving" solution?
FYI:
Model artifact is, usually, a weights/model file. The idea that later you will be able to access it and serve it. Now the problem is (and I think this is what you are referring to) there is usually a specific piece of code tied to that model that can use it (a.k.a pyfunc)
A few ideas:
These days everyone is trying to build their models with generic interface, so that scikit learn will be able to serve any model it was storing. (tf serving and pytorch script are of similar nature). If this is the case the Model framework could be used in order to detect which of these frameworks is to be used (this could actually be done in runtime) You could pickle the function itself and store it as a second artifact (basically upload_artifcat could auto_pickle it for you). That said, pickling is quite fragile and you have to have all the function dependencies in order to unpickle it.WDYT?

BTW: on a diff note, the auto-archive of the HPO will probably be in the nest version due in a few days 😉