Preface 1. Training Data Introduction Training Data Intents What Can You Do With Training Data? What Is Training Data Most Concerned With? Training Data Opportunities Business Transformation Training Data Efficiency Tooling Proficiency Process Improvement Opportunities Why Training Data Matters ML Applications Are Becoming Mainstream The Foundation of Successful AI Training Data Is Here to Stay Training Data Controls the ML Program New Types of Users Training Data in the Wild What Makes Training Data Difficult? The Art of Supervising Machines A New Thing for Data Science ML Program Ecosystem Data-Centric Machine Learning Failures History of Development Affects Training Data Too What Training Data Is Not Generative AI Human Alignment Is Human Supervision Summary 2. Getting Up and Running Introduction Getting Up and Running Installation Tasks Setup Annotator Setup Data Setup Workflow Setup Data Catalog Setup Initial Usage Optimization Tools Overview Training Data for Machine Learning Growing Selection of Tools People, Process, and Data Embedded Supervision Human Computer Supervision Separation of End Concerns Standards Many Personas A Paradigm to Deliver Machine Learning Software Trade-Offs Costs Installed Versus Software as a Service Development System Scale Installation Options Annotation Interfaces Modeling Integration Multi-User versus Single-User Systems Integrations Scope Hidden Assumptions Security Open Source and Closed Source History Open Source Standards ���� 3.Schema 4.Data Engineering 5.Workflow 6.Theories, Concepts, and Maintenance 7.AI Transformation and Use Cases 8.Automation 9.Case Studies and Stories