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
