Summary
In this chapter, we introduced the SageMaker Studio features at a high level. We mapped the features to the phases of a typical ML life cycle and discussed why and how SageMaker is used in the ML life cycle. We set up a SageMaker Studio domain and executed our first-ever notebook in SageMaker Studio. We learned the infrastructure of the SageMaker Studio and how to pick the right kernel image and compute instance for a notebook. Lastly, we talked about the basic concepts behind the key tool, the SageMaker Python SDK, and how it interacts with the cloud and SageMaker, as this is the foundation to lots of our future activities inside SageMaker Studio.
In the next chapter, we will jumpstart our ML journey by preparing a dataset with SageMaker Data Wrangler for an ML use case. You will learn how easy it is to prepare and process your data in SageMaker Studio.
