Summary
In this chapter, we reviewed how SageMaker provides support for the ML and DL frameworks using Docker containers. After reading this chapter, you should now know how to select the most appropriate DL container usage pattern according to your specific use case requirements. We learned about SageMaker toolkits, which simplifies developing SageMaker-compatible containers. In later sections, you gained practical knowledge of how to develop custom containers and scripts for training and inference tasks on Amazon SageMaker.
In the next chapter, we will learn about the SageMaker development environment and how to efficiently develop and troubleshoot your DL code. Additionally, we will learn about DL-specific tools and interfaces that the SageMaker development environment provides to simplify the building, deploying, and monitoring of your DL models.