AWS - Machine Learning Engineer Associate Study Guide

1: Data Preparation for Machine Learning (ML)

This chapter covers the ingestion, storage, transformation, feature engineering, and integrity checks required to prepare data for machine learning workloads on AWS. It focuses on Amazon SageMaker native tooling (Data Wrangler, Feature Store, Ground Truth, Clarify), the surrounding data services (S3, Glue, EMR, Kinesis, MSK), and the governance controls (KMS, Macie, VPC) that make an ML dataset production ready.