Summary
Built-in frameworks are extremely useful, but sometimes you need something a little—or very—different. Whether you're starting from the built-in containers or starting from scratch, SageMaker lets you build your training and deployment containers exactly the way you want them. Freedom for all!
In this chapter, you learned how to customize Python and R containers both for training and deployment. You saw how you could use them with the SageMaker SDK and its usual workflow. You also learned about two nice open source tools, MLflow and Sagify, and how you can train and deploy models using only a command-line interface.
This concludes our extensive coverage of modeling options on SageMaker: built-in algorithms, built-in frameworks, and custom code. In the next chapter, you'll learn about SageMaker features that help you to scale your training jobs.
