Model versioning is a data problem

We kept having the same argument: which model is in production, and why does it behave differently from staging?

The answer was almost never the model file. It was the data.

What we changed:

• Every training run records a hash of the input dataset.
• The serving layer logs the model version and the feature version.
• A small registry maps model-version -> data-hash -> metrics.

That last piece is just a table, but it ended most of the "why is it different" conversations.

The model is the easy part to version. The data is where the surprises live.

Side quest: I'm learning Zig because I want to understand how the fast inference runtimes are built. If you've read any of them, I'd take recommendations.