[aaron-santos]

Sure, I'm thinking about the development lifecycle in terms of what actions data scientists have to take to get a model deployed. Anytime the process has a branch (ie: you need to change this file whenever something elsewhere changes) then I know I'm going to forget to do that.

If we were to use Cortex, we would likely wrap the creation of cortex.yml in a function and call it when we're saving our models. We do something similar right now and store the meta in json files for later deployment. I love tracking config in git too.

[ospillinger]

That makes sense. Programmatically updating cortex.yaml is a common use case especially when you're thinking about continuous deployment. We also have a Python client which can replace the cortex.yaml file (https://www.cortex.dev/deployments/python-client).