Foundations of ML Data Engineering
Understand how to transform raw data into model-ready features through data cleaning, scaling, and encoding techniques. Learn which AWS services best support these transformations and how to optimize data formats and quality for machine learning models.
Raw data sitting in Amazon S3 buckets, streaming from Kinesis, or exported from relational databases is almost never ready for machine learning. ML algorithms expect numerical, consistently structured, and statistically sound inputs. The gap between raw ingestion and model training is where ML data engineering operates, and understanding this gap is a high-value skill tested on the AWS Certified Machine Learning Engineer – Associate exam.
This lesson establishes the strategic framework you need before diving into any specific AWS service. Rather than jumping straight into AWS Glue or SageMaker, you will first build a mental model of which transformations are required, why they matter, and which tool fits each pattern.
Four AWS services dominate the data engineering stage of the ML life cycle on AWS.
AWS Glue handles programmatic, high-volume ETL with built-in schema discovery.
AWS Glue DataBrew provides a visual, no-code interface for data profiling and cleaning.
Amazon EMR with Apache Spark delivers massive-scale distributed processing with full cluster control.
Amazon SageMaker Data Wrangler is purpose-built for ML-specific exploratory data analysis and feature flows within the SageMaker ecosystem.
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