Analyzing large amounts of structured and unstructured data

Up until this point in the chapter, we have reviewed some of the typical methods for large-scale data analysis and introduced some of the key AWS services that focus on making the analysis task easier for users. In this section, we will practically introduce Amazon SageMaker as a comprehensive service that allows both the novice as well as the experienced ML practitioner to perform these data analysis tasks.

While SageMaker is a fully managed infrastructure provided by AWS along with tools and workflows that cater to each step of the ML process, it also offers a fully Integrated Development Environment (IDE) specifically for ML development called Amazon SageMaker Studio (https://aws.amazon.com/sagemaker/studio/). SageMaker Studio provides a data scientist with the capabilities to develop, manage, and view each part of the ML life cycle, including exploratory data analysis.

But, before jumping into a hands-on example...