The company already has custom Python training scripts, proprietary datasets, and uses PyTorch, with significant domain-specific logic embedded in the model. The goal is to migrate these workloads to AWS with the least development effort.
According to AWS documentation, Amazon SageMaker AI script mode is explicitly designed for this scenario. Script mode allows customers to bring their existing training scripts with minimal or no code changes and run them using prebuilt SageMaker framework containers, including PyTorch. This approach eliminates the need to redesign models or rewrite training logic while still benefiting from SageMaker’s managed infrastructure, scalability, monitoring, and security.
Option A is incorrect because SageMaker built-in algorithms require adapting data formats and training logic to AWS-provided implementations, which would increase development effort and may not support proprietary domain logic.
Option C is also incorrect because building and maintaining a custom container requires additional effort for containerization, dependency management, security updates, and lifecycle maintenance—making it more complex than necessary.
Option D is not viable because purchasing models from AWS Marketplace would not support the company’s proprietary datasets or unique domain knowledge embedded in existing models.
Therefore, using SageMaker AI script mode with prebuilt PyTorch containers is the fastest, most efficient, and AWS-recommended migration path that minimizes development effort while preserving existing workflows.
