A company runs custom PyTorch training scripts and proprietary datasets on-premises and needs to move these models to AWS with minimal effort. Which approach requires the least work to migrate?

Using SageMaker script mode with SageMaker-provided framework containers is the most efficient migration path because it allows you to run your existing PyTorch training scripts with minimal modifications. SageMaker provides pre-built Docker images for popular frameworks like PyTorch, handling the underlying infrastructure and dependencies. You simply point SageMaker to your script and data. Using SageMaker built-in algorithms would require rewriting your training logic to fit the algorithm's specific input/output formats and parameters, which is significant effort. Building and managing a custom container, while offering flexibility, adds the overhead of Dockerfile creation, image building, and maintenance, increasing effort. Buying models from AWS Marketplace is not a migration of existing models; it's acquiring new, potentially different models, which doesn't meet the requirement of moving their models.