Summary

Data needs to be cleaned, analyzed, and prepared before it is used to train ML models. Since it takes time and effort to work on these types of requirements, it is recommended to use no-code or low-code solutions such as AWS Glue DataBrew and Amazon SageMaker Data Wrangler when analyzing and processing our data. In this chapter, we were able to use these two services to analyze and process our sample dataset. Starting with a sample “dirty” dataset, we performed a variety of transformations and operations, which included (1) profiling and analyzing the data, (2) filtering out rows containing invalid data, (3) creating a new column from an existing one, (4) exporting the results into an output location, and (5) verifying whether the transformations have been applied to the output file.

In the next chapter, we will take a closer look at Amazon SageMaker and we will dive deeper into how we can use this managed service when performing machine learning experiments...