RE: DOI - Yes I am—ahem, to some degree—academically inclined also.
One DOI wouldn’t be enough, oftentimes algorithms are made up of a multitude. Also versioning would still be needed, for new dependent framework support (e.g., TensorFlow 2 for a previously TF 1 implementation of DOI) and for bug fixing (sometimes even using different # bit types).
As for PEP301 1, I don’t see why new classifiers couldn’t be added for machine learning, artificial intelligence, specific machine learning frameworks (& versions), and whence in the hierarchy the given pip installable package sits. It doesn’t hurt that Google is funding some PyPi stuff now!
Now again this is just one proposal for how to handle the problem of high agility of new research and low consumption by industry and academia.
A related solution is to release packages to a packages [dot] tensorflow [dot] org [or similar] domain (which pip install supports with its rc or --index-url option). Short term and development-mode solution is to release it to the packages section of your source repository (e.g., releases on GitHub).
Unrelated solutions I am open to, and would welcome ideas.