Training Data for Machine Learning: Human Supervision from Annotation to Data Science

This book delves into the critical discipline of training data for machine learning, exploring how raw data—from videos and images to text and geospatial information—is transformed into meaningful inputs for AI models. It covers foundational concepts, day-to-day practices, and efficiency improvements in training data preparation.

The work provides practical insights into the art of shaping and annotating data, emphasizing human supervision throughout the process. Readers will learn to record intelligence in reproducible ways for ML applications, with real-world case studies illustrating key principles.

As an Early Release edition, it offers raw, unedited content from the author, allowing early access to evolving methodologies. The book aims to equip teams with strategies to become more AI/ML-centric, addressing both creation and refinement of training datasets.