Ground Truth and Human-in-the-Loop

Supervised machine learning models learn by mapping inputs to known, correct outputs, and the quality of those outputs (the labels) determines how well a model generalizes. In the AWS ecosystem, building a reliable ML pipeline requires more than training algorithms. It involves creating accurate labeled datasets, incorporating human review when needed, and validating data quality before it reaches training. For the AWS Certified Machine Learning Engineer – Associate exam, it’s important to understand how three services support different parts of the ML workflow across data preparation, validation, and human-in-the-loop inference. Amazon SageMaker Ground Truth handles scalable dataset labeling. Amazon Augmented AI (A2I) provides human review for selected model or AWS AI service outputs. AWS Glue Data Quality enforces automated validation rules in ETL and AWS Glue Data Catalog workflows. This lesson explains how these services function and how data flows between them.

For the AWS MLA-C02 exam, these services represent different parts of the ML life cycle: Ground Truth focuses on creating labeled training ...