ML Model Deployment Checklist

Deploying ML models to production requires careful planning and testing.

Pre-Deployment Checklist

[ ] Model performance meets business requirements

[ ] Tested on diverse, representative data

[ ] Edge cases and failure modes identified

[ ] Model bias and fairness evaluated

[ ] Scalable model serving infrastructure

[ ] API design and documentation

[ ] Monitoring and alerting configured

[ ] Rollback strategy defined

[ ] Data validation and quality checks

[ ] Feature store or preprocessing pipeline

[ ] Data versioning strategy

[ ] Drift detection implemented

[ ] Unit tests for data processing

[ ] Integration tests for full pipeline

[ ] Load testing completed

[ ] Shadow mode testing successful

[ ] Model card with performance metrics

[ ] API documentation

[ ] Runbooks for common issues

[ ] Training documentation for users

Monitor model performance continuously

Track data drift and model degradation

Plan for model retraining cadence

Gather user feedback and iterate