Summary

In this chapter, you discovered the main capabilities of Amazon SageMaker, and how they can help solve your machine learning pain points. By providing you with managed infrastructure and pre-installed tools, SageMaker lets you focus on the machine learning 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 and in Amazon SageMaker Studio. The latter is a managed machine learning IDE where many other SageMaker capabilities are just a few clicks away.

Finally, you learned about Amazon SageMaker JumpStart, a collection of machine learning solutions and state-of-the-art models that you can deploy in one click, and start testing in minutes.

In the next chapter, we'll see how you can use Amazon SageMaker and other AWS services to prepare your datasets for training.