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How would you implement an MLOps pipeline on AWS using SageMaker, CodePipeline, and Lambda?

The AWS ML Engineer staple. The answer that scores is a clean division of labor, CodePipeline owns code, SageMaker Pipelines owns the ML DAG, the Model Registry is the handoff point, plus the cross-account detail that separates real builds from doc reading.

Updated Sep 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.

The AWS ML Engineer staple. The answer that scores is a clean division of labor, CodePipeline owns code, SageMaker Pipelines owns the ML DAG, the Model Registry is the handoff point, plus the cross-account detail that separates real builds from doc reading.

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UP NEXT ON YOUR JOURNEY
Next in this trackExplain MLOps on Azure, how do Azure DevOps and Azure ML Pipelines fit together?Next in this trackYour pipeline retrains automatically. How do you decide whether the new model replaces the old one?Next in this trackDesign an automated retraining pipeline. What should trigger retraining?
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