Chapter 2: Handling Data Preparation Techniques

Data is the starting point of any machine learning project, and it takes lots of work to turn data into a dataset that can be used to train a model. That work typically involves annotating datasets, running bespoke scripts to preprocess them, and saving processed versions for later use. As you can guess, doing all this work manually, or building tools to automate it, is not an exciting prospect for machine learning teams.

In this chapter, you will learn about AWS services that help you build and process data. We'll first cover Amazon SageMaker Ground Truth, a capability of Amazon SageMaker that helps you quickly build accurate training datasets. Then, we'll talk about Amazon SageMaker Processing, another capability that helps you run your data processing workloads, such as feature engineering, data validation, model evaluation, and model interpretation. Finally, we'll discuss other AWS services that may help with data analytics: Amazon Elastic Map Reduce, AWS Glue, and Amazon Athena: