Core

CI/CD for Models

Model CI/CD looks like code CI/CD but ships data, weights, and prompts together, and its merge gate is an eval suite against a golden set, not a passing unit test. The pipeline trains, evaluates, registers, soaks in shadow or canary, then promotes, with every input versioned so any release is reproducible.

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RELATED CONCEPTS

Model Registry and Promotion Data and Concept Drift A/B, Canary and Shadow Testing Golden Datasets and Eval Sets

LESSONS THAT TEACH THIS

Hard DeploymentsFind the dependencies your offline build and first request still need30 min→The FDE EngagementRecreate the release and prove that its state can recover28 min→

PRACTICE THIS IN REAL QUESTIONS

MLOps & ML EngineeringHow is CI/CD for ML models different from traditional DevOps CI/CD?→ML System Design (Product)Design an evaluation framework for an ads-ranking system.→System Design & Production EngineeringIt's Friday evening at a customer site and they want a hotfix shipped now. How do you do it safely, and when do you refuse?→MLOps & ML EngineeringWhat's the difference between shadow deployment and A/B testing a model?→MLOps & ML EngineeringWhen you say you 'push staging to production,' what does that actually mean for a model?→MLOps & ML EngineeringHow did you ensure quality and testing on top of your MLOps pipeline?→