ML Model Deployment Checklist

When to Use This Checklist

Use this checklist before promoting a trained machine learning model into a production serving environment. A model that scores well offline can still fail in production due to training/serving skew, unbounded latency, or missing rollback. This list covers the operational gates that separate a notebook experiment from a dependable service.

How to Use This Checklist

Start by confirming the artifact is versioned and reproducible from pinned data and code, since you cannot operate what you cannot rebuild. Validate offline metrics against agreed thresholds, then verify the serving feature pipeline matches training to prevent skew. Always deploy through a canary or shadow stage so real traffic validates the model before it carries full load. Treat drift detection and automated rollback as non-negotiable before sign-off.

What Good Looks Like

A well-deployed model is reproducible, observable, and reversible. The serving endpoint meets latency and throughput targets under load tests, authenticates callers, and validates inputs. Drift detection monitors inputs and predictions and alerts on degradation. A model card documents inputs, outputs, and limitations. Rollback to the previous version is automated and tested, and predictions are logged with request IDs for auditing and future retraining.

Common Pitfalls

Training/serving skew is the most frequent silent failure: features are computed differently at inference than during training. Teams often ship without drift monitoring, so accuracy erodes unnoticed. Skipping load tests leads to timeouts at peak. Missing rollback turns a bad release into an outage. Finally, undocumented models block compliance review and make incident response slow.

Related Resources

Consult MLOps principles, model monitoring and drift detection guidance, feature store practices, data version control, and canary release patterns.