An agriculture company wants to improve crop yield forecasting for the upcoming season by using...

To compare a custom scikit-learn model with a benchmark model in a managed environment, the most effective and maintainable solution is to extend an existing SageMaker scikit-learn container.

“If you are using a framework like scikit-learn and need custom logic, you can extend the prebuilt SageMaker containers to include your own inference or training scripts. AWS manages the container base and you only maintain your code.”

This approach allows the data scientist to maintain control over model logic while letting SageMaker handle the container lifecycle, scaling, and infrastructure management—which aligns with the requirement for AWS to handle operational maintenance.