A lean checklist for shipping your first ML model

Shipping a model is less about the model itself and more about everything around it. A small, reliable checklist prevents most production surprises.

Data Validation

Validate schema and ranges before inference. Fail loudly when inputs drift outside expectations.

Reproducibility

Pin dependencies, store model artifacts, and keep the training configuration next to the code.

Monitoring & Drift

Track input distributions and prediction quality. Drift alerts are cheaper than silent failures.