From pilot to production: an MLOps checklist

Most models never leave the notebook. The gap between a promising experiment and a reliable production service is MLOps — the engineering discipline that makes ML dependable.

Before you ship

• Reproducible training — versioned data, code and parameters so any result can be rebuilt.
• A real evaluation set — know what “good enough” means, with numbers, before launch.
• A serving plan — batch, real-time or streaming? Each has different infrastructure needs.

Once it’s live

1. Monitor predictions, latency and errors like any other production service.
2. Watch for drift — when the world changes, model quality quietly degrades.
3. Automate retraining so refreshes are routine, not a research project.
4. Keep a rollback — models are deploys too, and deploys sometimes go wrong.

The mindset shift

Treat models as products, not experiments. The teams that win with AI aren’t the ones with the cleverest notebook — they’re the ones who can ship, monitor and improve models on a schedule.