Is there any guideline or best practice for storing machine learning models? We can store them as binary files. However, machine learning model is more than model artifact (it also contains data, code, hyperparameters, metrics). Wonder if there is any practice around integrating Artifactory with CI/CD process (using to manage ML model artifact/metadata, supporting automated model promotion and human-in-the-loop promotion)?
The only article touching this topic very lightly is: https://towardsdatascience.com/who-moved-my-binaries-7c4d797cd783