A holistic and real-world approach to operationalizing artificial intelligence in your company In Operating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jägare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You'll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including key areas such as; data mesh, data fabric, aspects of security, data privacy, data rights and IPR related to data and AI models. In the book, you’ll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI) The importance of efficient and reproduceable data pipelines, including how to manage your company's data An operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production using CI/CD/CT techniques, that generates value in the real world Key competences and toolsets in AI development, deployment and operations What to consider when operating different types of AI business models With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real world—and not just the lab—Operating AI is a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment.

Table of Contents:
Foreword xii 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