ML Model Deployment Checklist

What this checklist is

A checklist for taking a trained machine learning model into production and keeping it healthy there. It covers baselines and metrics, training and serving consistency, offline validation, packaging and the model registry, staged rollout and rollback, monitoring for skew and drift, and ownership, with a severity and a verification step for each check.

It has 7 chapters and 30 checks, each with how to verify it and how much it matters. For engineers putting ML models into production.

Chapter by chapter

1. Baselines, metrics and scope (4 checks)

Decide what the model must beat and how you will measure it before you deploy anything. These checks set the bar the rest of the list measures against.

2. Features and training-serving consistency (5 checks)

A model only performs in production as well as the features it receives there. These checks keep training and serving features the same.

3. Offline validation before release (4 checks)

Offline checks are the cheapest place to stop a bad model. These checks compare the candidate with the live model on data that looks like production.

4. Packaging, registry and reproducibility (5 checks)

Every model in production should be traceable to its code, data and run. These checks make a model version something you can inspect, reproduce and roll back.

5. Rollout and rollback (4 checks)

Offline wins do not always survive real traffic. These checks expose a new model to production gradually and make it easy to back out.

6. Monitoring, skew and drift (4 checks)

Models decay quietly as the world changes. These checks make that decay visible and connect it to retraining.

7. Ownership, documentation and governance (4 checks)

Models in production need owners and a record of what they are for. These checks follow the Model Cards paper and NIST's AI Risk Management Framework.

How it works on Sefism

The checklist is one page: each check has a severity (must, should or nice to have) and a concrete way to verify it, and members can tick checks off as they go. The opening chapters are open to everyone. Adding it to your account uses credits, opens every chapter and keeps your progress.

Every claim is checked against the sources listed on the page (8 cited).