Standard MLOps advice assumes you can retrain and promote a model the moment a metric improves. Pharma environments add a wrinkle: models feeding commercial forecasts or anything touching regulated decisions need change-control sign-off, documented validation, and a paper trail — none of which a typical CI/CD pipeline produces on its own.
This post is a placeholder for a write-up on layering an approval gate onto an MLflow/Databricks pipeline: versioned features, reproducible training runs, and validation artifacts generated automatically instead of assembled by hand after the fact.