Production
Getting a model out of the notebook and keeping it working once real traffic hits it.
6 articles, newest first.
- Incremental tables that do not drift Full rebuilds stop being affordable, incremental builds start being wrong. How to pick the incremental key, handle late data, and catch drift before a stakeholder does.
- Your model started dying the day you deployed it Data drift, concept drift, and delayed labels: a minimal monitoring setup that catches decay before the business does, without buying a platform.
- From notebook to production without a rewrite Notebooks are where analysis is born, not where it should live. A low-drama path: extract functions, add tests for data logic, parameterise, and schedule a script.
- Schema checks: make your pipeline refuse bad data Upstream will change without telling you. Explicit schema and expectation checks at pipeline boundaries turn silent corruption into loud, early failures.
- Reproducibility: the checklist nobody follows until it burns them Seeds, pinned environments, data snapshots, and one-command reruns — the practical minimum for results you can regenerate in six months.
- Feature engineering that survives contact with production A feature that boosts your offline metrics but cannot be computed at prediction time is worse than useless. How to build features that keep working after deployment.