What changes when you put an ML model into production?

Production ML needs reliability beyond accuracy. Consider: - Feature pipelines and consistency at inference - Monitoring drift and data quality - Latency and scaling - Retraining cadence - Explainability and governance A good handoff includes model versioning, reproducible training, and clear rollback strategies.