How to version control your production machine learning models Algorithmia Blog

Machine learning is about rapid experimentation and iteration, and without keeping track of your modeling history you won't be able to learn much. Versioning lets you keep track of all of your models, how well they've done, and what hyperparameters you used to get there. This post will walk through why data versioning is important, tools to get it done with, and how to version your models that go into production. If you've spent time working with Machine Learning, one thing is clear: it's an iterative process. There are so many different parts of your model--how you use your data, hyperparameters, parameters, algorithm choice, architecture--and the optimal combination of all of those is the holy grail of machine learning.