Training Data Explained for Generative AI models (Lesson 19)

Training data, machine learning data types, labeled vs unlabeled data, structured vs unstructured data on AWS explained simply. In this video, we break down one of the most important concepts for the AWS AI Practitioner certification: training data—what it is, why data quality matters, and how AWS services handle it. You’ll learn: Why data quality (garbage in, garbage out) is the golden rule of machine learning The difference between labeled vs unlabeled data and how they map to supervised vs unsupervised learning What structured and unstructured data really means with real-world examples Where training data lives on AWS, including Amazon S3, SageMaker, and Ground Truth Which AWS services to use for text, images, and tabular data (Comprehend, Rekognition, RDS, Redshift) This video focuses on conceptual clarity, exam-ready insights, and real AWS context—no math, no code, just what you actually need to understand training data in machine learning. 🚀 Easy explanation. Exam-focused. Real AWS workflow. 📌 Timestamps: Find me here LinkedIn - https://www.linkedin.com/in/girish-mukim/ Website - https://imaginetechverse.com/ Twitter - https://twitter.com/GirishMukim YouTube - https://www.youtube.com/@AWSLearn