@Bhack Interesting find, I emailed the A/Professor and my last reply was:

I think it is possible to do what you propose, with a pip install​ style approach; whether or not released on PyPi.
The idea is to think of the ML, AI, and data-science tasks as not their own pipeline but as just part of the regular CI/CD pipeline (commonly with additional constraints to deploy to better hardware).
A huge problem in the industry and academia is the sheer quantity of new research coming out. This problem is exacerbated by the lack of standardisation in how these new transfer learning models, optimisers, loss functions, &etc. are exposed for others to consume. Usually it’s Jupyter Notebooks, or if you’re lucky maybe some .py files with absolute paths.
Almost never does one find a random research paper with an actual decent package; let alone expecting documentation and tests.
By standardising how the new model/optimiser/&etc. is exposed for consumption—e.g., setuptools and publish to PyPi; implementing an abstractclass​ that is ML-framework independent—one would solve two entire classes of problems, enabling the ML frameworks and new algorithms to be used as a database from which to experiment with new datasets, models, optimisers, &etc. off of.
Which, IMHO, would welcome in a new era of data science / machine learning / artificial intelligence for both research and industry.

@mihaimaruseac Not sure how the DOI would apply. If you’re thinking about the PyPi classifier… I envision a simple version number à la Django, Python, &etc.: Classifiers · PyPI (and additionally something more specific, like Machine Learning :: Optimizers