Intro
Preface
Contents
About the Authors
1 Introduction
1.1 Building Blocks of AI
1.2 AI Current State
1.3 Motivation
1.4 Need for Paradigm Shift from Model-Centric AI to Data-Centric AI
1.5 Summary
References
2 Model-Centric AI
2.1 Working Principle
2.1.1 Supervised Learning
2.1.2 Unsupervised Learning
2.1.3 Reinforcement Learning
2.2 Learning Methods
2.2.1 Supervised Machine Learning Algorithms
2.2.2 Unsupervised Machine Learning Algorithms
2.2.3 Deep Learning Algorithms
2.3 Model Building
2.4 Model Training
2.5 Model Testing

2.6 Model Tuning
2.7 Use Cases: Model-Centric AI
2.8 Summary
References
3 Data-Centric Principles for AI Engineering
3.1 Overview
3.2 AI Engineering
3.3 Challenges
3.4 Data-Centric Principles
3.5 Summary
References
4 Mathematical Foundation for Data-Centric AI
4.1 Overview
4.1.1 Statistics
4.1.2 Linear Algebra
4.1.3 Calculus
4.1.4 Probability Theory
4.1.5 Multivariate Calculus
4.1.6 Graph Theory
4.2 Statistical Data Analysis
4.3 Data Tendency and Distribution
4.3.1 Data Tendency/Measure of Central Tendency

4.3.2 Measure of Dispersion
4.3.3 Data Distribution
4.4 Data Models
4.5 Optimization Techniques
4.6 Summary
References
5 Data-Centric AI
5.1 Data Acquisition
5.1.1 The Data Acquisition Process
5.1.2 Key Insights for Big Data Acquisition
5.1.3 Case Study: Data Acquisition for Retail Company
5.2 Data Labeling
5.2.1 How Does Data Labeling Work?
5.2.2 Data Labeling Approaches
5.2.3 Importance of Data Labeling
5.2.4 Case Study: Data Labeling for Autonomous Vehicle Training
5.3 Data Annotation
5.3.1 Types of Data Annotation

5.3.2 Case Study on Data Annotation
5.4 Data Augmentation
5.4.1 How Does Data Augmentation Work?
5.4.2 Case Study on Data Augmentation
5.5 Data Deployment
5.5.1 Case Study on Data Deployment
5.6 Data-Centric AI Tools
5.6.1 Case Study: Predicting Customer Churn for a Telecommunications Company
5.7 Summary
References
6 Data-Centric AI in Healthcare
6.1 Overview
6.2 Need and Challenges of Data-Centric Approach
6.3 Application Implementation in Data-Centric Approach
6.4 Application Implementation in Model-Centric Approach

6.5 Comparison of Model-Centric AI and Data-Centric AI
6.6 Summary
References
7 Data-Centric AI in Mechanical Engineering
7.1 Overview
7.2 Need and Challenges of Data-Centric Approach
7.3 Application Implementation in Data-Centric Approach
7.4 Application Implementation in Model-Centric Approach
7.5 Comparison of Model-Centric AI and Data-Centric AI
7.6 Case Study: Mechanical Tools Classification
7.7 Summary
References
8 Data-Centric AI in Information, Communication and Technology
8.1 Overview
8.2 Need and Challenges of Data-Centric Approach