Data Prep for Generative AI with SageMaker Data Wrangler and Ground Truth

High-quality data is the foundation of any successful generative AI system, yet preparing and labeling that data remains one of the most challenging parts of the pipeline. This course teaches you how to leverage AWS SageMaker Data Wrangler and SageMaker Ground Truth to streamline your data workflows. Through clear, written explanations and practical step-by-step guides, you will transition from manual data manipulation to automated, scalable data preparation. You will learn how to clean unstructured data, orchestrate human-in-the-loop validation, and format datasets specifically for training and fine-tuning large language models. What you will learn: Understand the foundational concepts of data preparation and human-in-the-loop validation for machine learning; Configure SageMaker Data Wrangler to import, clean, and transform unstructured text data without writing complex code; Design data labeling workflows using SageMaker Ground Truth to build high-quality instruction-tuning datasets; Apply data quality checks and validation techniques to ensure your training data is free from bias and errors; Prepare specialized datasets for generative AI use cases, including retrieval-augmented generation and prompt-response formatting; Integrate human feedback loops to continuously improve model performance and safety. The course begins with essential definitions and data engineering fundamentals before guiding you through step-by-step configurations of Data Wrangler and Ground Truth. You will progress from raw data ingestion to deploying fully prepared datasets ready for generative AI training. This course is designed for aspiring data engineers, AI developers, and cloud enthusiasts who want to master data preparation on AWS. No prior experience with SageMaker or advanced machine learning is required, though a basic familiarity with cloud concepts is helpful. Start building robust data pipelines for your AI projects today.