In this chapter, you discovered the main capabilities of Amazon SageMaker, and how they help solve your ML pain points. By providing you with managed infrastructure and pre-installed tools, SageMaker lets you focus on the ML problem itself. Thus, you can go more quickly from experimenting with models to deploying them in production.
Then, you learned how to set up Amazon SageMaker on your local machine, on a notebook instance, and on Amazon SageMaker Studio. The latter is a managed ML IDE where many other SageMaker capabilities are just a few clicks away.
In the next chapter, we'll see how you can use Amazon SageMaker and other AWS services to prepare your datasets for training.