Best practices for model promotion

We are building app and model code in a dev environment (container image). Now when it is about to switch to production, we build a new image. For app code this would be fine, but what would be a best practice for the model? Experiments / runs are tracked in the dev environment (mlflow) and so are all training artifacts like the model or data. A new training in the prod environment does not really make sense I guess, since it would not be the same model anymore and the previous evaluated model would be in dev model registry but not in prod. So I cannot reference it since the prod app should on