[aaron-santos]

> The tradeoff has been theres a step between notebook and production for ML engineers which can slow them down, but it forces code review and increases the number of tests checked in.

This was a game-changer for us. What does your testing story look like?

[__erik]

We're still ironing out a few things but unit tests for various functions then we have smol statistically representative datasets for each model. In CI we train a model on the small dataset (aim for <5 mins e2e) then have a a suite of model metrics we care about, tests confirm values are within acceptable bounds and the test values are pulled into the PR.