Introduction xv

Chapter 1 Balancing the AI Investment 1

Defining AI and Related Concepts 3

Operational Readiness and Why It Matters 8

Applying an Operational Mind- set from the Start 12

The Operational Challenge 15

Strategy, People, and Technology Considerations 19

Strategic Success Factors in Operating AI 20

People and Mind- sets 23

The Technology Perspective 28

Chapter 2 Data Engineering Focused on AI 31

Know Your Data 32

Know the Data Structure 32

Know the Data Records 34

Know the Business Data Oddities 35

Know the Data Origin 36

Know the Data Collection Scope 37

The Data Pipeline 38

Types of Data Pipeline Solutions 41

Data Quality in Data Pipelines 44

The Data Quality Approach in AI/ML 45

Scaling Data for AI 49

Key Capabilities for Scaling Data 51

Introducing a Data Mesh 53

When You Have No Data 55

The Role of a Data Fabric 56

Why a Data Fabric Matters in AI/ML 58

Key Competences and Skillsets in Data Engineering 60

Chapter 3 Embracing MLOps 71

MLOps as a Concept 72

From ML Models to ML Pipelines 76

The ML Pipeline 78

Adopt a Continuous Learning Approach 84

The Maturity of Your AI/ML Capability 86

Level 0— Model Focus and No MLOps 88

Level 1— Pipelines Rather than Models 89

Level 2— Leveraging Continuous Learning 90

The Model Training Environment 91

Enabling ML Experimentation 92

Using a Simulator for Model Training 94

Environmental Impact of Training AI Models 96

Considering the AI/ML Functional Technology Stack 97

Key Competences and Toolsets in MLOps 103

Clarifying Similarities and Differences 106

MLOps Toolsets 107

Chapter 4 Deployment with AI Operations in Mind 115

Model Serving in Practice 117

Feature Stores 118

Deploying, Serving, and Inferencing Models at Scale 121

The ML Inference Pipeline 123

Model Serving Architecture Components 125

Considerations Regarding Toolsets for Model Serving 129

The Industrialization of AI 129

The Importance of a Cultural Shift 139

Chapter 5 Operating AI Is Different from Operating Software 143

Model Monitoring 144

Ensuring Efficient ML Model Monitoring 145

Model Scoring in Production 146

Retraining in Production Using Continuous Training 151

Data Aspects Related to Model Retraining 155

Understanding Different Retraining Techniques 156

Deployment after Retraining 159

Disadvantages of Retraining Models Frequently 159

Diagnosing and Managing Model Performance Issues in Operations 161

Issues with Data Processing 162

Issues with Data Schema Change 163

Data Loss at the Source 165

Models Are Broken Upstream 166

Monitoring Data Quality and Integrity 167

Monitoring the Model Calls 167

Monitoring the Data Schema 168

Detecting Any Missing Data 168

Validating the Feature Values 169

Monitor the Feature Processing 170

Model Monitoring for Stakeholders 171

Ensuring Stakeholder Collaboration for Model Success 173

Toolsets for Model Monitoring in Production 175

Chapter 6 AI Is All About Trust 181

Anonymizing Data 182

Data Anonymization Techniques 185

Pros and Cons of Data Anonymization 187

Explainable AI 189

Complex AI Models Are Harder to Understand 190

What Is Interpretability? 191

The Need for Interpretability in Different Phases 192

Reducing Bias in Practice 194

Rights to the Data and AI Models 199

Data Ownership 200

Who Owns What in a Trained AI Model? 202

Balancing the IP Approach for AI Models 205

The Role of AI Model Training 206

Addressing IP Ownership in AI Results 207

Legal Aspects of AI Techniques 208

Operational Governance of Data and AI 210

Chapter 7 Achieving Business Value from AI 215

The Challenge of Leveraging Value from AI 216

Productivity 216

Reliability 217

Risk 218

People 219

Top Management and AI Business Realization 219

Measuring AI Business Value 223

Measuring AI Value in Nonrevenue Terms 227

Operating Different AI Business Models 229

Operating Artificial Intelligence as a Service 230

Operating Embedded AI Solutions 236

Operating a Hybrid AI Business Model 239

Index 241