Iterate

A model that performs in the lab has finished rehearsing, not performing. Iterate prepares it for the environment that actually matters — production — and ships it deliberately.

What happens in this phase

• Optimize for the target environment. The model is tuned to run efficiently where it will actually live: latency, cost, and hardware constraints become first-class requirements.
• Plan the deployment like a project. Infrastructure, security, and integration with existing systems are engineered — not discovered during rollout.
• Prepare the humans. User training materials and support channels exist before go-live, so adoption doesn’t stall on confusion.
• De-risk the launch. Deployment strategy covers scale, staged rollout, and an explicit rollback plan — confidence comes from having a way back.

Watch out for

• The lab-to-production cliff: a model validated on clean batch data meeting messy real-time inputs for the first time on launch day.
• Deployments without rollback plans. Hope is not a rollback plan.
• Shipping the model but not the operating model — if support and training lag, users experience a broken product no matter how good the model is.

Method spotlight

Iterate runs on MLOps-DevOps Deployment — the cycle of deployment planning, CI/CD pipeline, staging tests, production deployment, monitoring, and transparent communication. Diagram on the framework page.

Go deeper

Chapters 18–21 cover CI/CD and the ML-DevOps marriage in practical detail — free here from September 2026, or in the full edition on Amazon today.