Engineering AI Systems

Name: Engineering AI Systems
Author: Len Bass

Architecture and DevOps Essentials

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E-book, blz. | Engels

Pearson Education | e druk, 2025

ISBN13: 9780138261498

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Pearson Education e druk, 2025 9780138261498

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Master the Engineering of AI Systems: The Essential Guide for Architects and Developers

In today's rapidly evolving world, integrating artificial intelligence (AI) into your systems is no longer optional. Engineering AI Systems: Architecture and DevOps Essentials is a comprehensive guide to mastering the complexities of AI systems engineering. This book combines robust software architecture with cutting-edge DevOps practices to deliver high-quality, reliable, and scalable AI solutions.

Experts Len Bass, Qinghua Lu, Ingo Weber, and Liming Zhu demystify the complexities of engineering AI systems, providing practical strategies and tools for seamlessly incorporating AI in your systems. You will gain a comprehensive understanding of the fundamentals of AI and software engineering and how to combine them to create powerful AI systems. Through real-world case studies, the authors illustrate practical applications and successful implementations of AI in small- to medium-sized enterprises across various industries, and offer actionable strategies for designing, building, and operating AI systems that deliver real business value. Lifecycle management of AI models, from data preparation to deployment Best practices in system architecture and DevOps for AI systems System reliability, performance, and security in AI implementations Privacy and fairness in AI systems to build trust and achieve compliance Effective monitoring and observability for AI systems to maintain operational excellence Future trends in AI engineering to stay ahead of the curve

Equip yourself with the tools and understanding to lead your organization's AI initiatives. Whether you are a technical lead, software engineer, or business strategist, this book provides the essential insights you need to successfully engineer AI systems.

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Specificaties

ISBN13:9780138261498

Taal:Engels

Bindwijze:e-book

Uitgever:Pearson Education

Inhoudsopgave

Preface xiii Acknowledgments xvii About the Authors xix Chapter 1: Introduction 1 1.1 What We Talk about When We Talk about Things: Terminology 2 1.2 Achieving System Qualities 4 1.3 Life-Cycle Processes 6 1.4 Software Architecture 10 1.5 AI Model Quality 13 1.6 Dealing with Uncertainty 19 1.7 Summary 20 1.8 Discussion Questions 21 1.9 For Further Reading 21 Chapter 2: Software Engineering Background 23 2.1 Distributed Computing 23 2.2 DevOps Background 35 2.3 MLOps Background 42 2.4 Summary 44 2.5 Discussion Questions 45 2.6 For Further Reading 45 Chapter 3: AI Background 47 3.1 Terminology 48 3.2 Selecting a Model 49 3.3 Preparing the Model for Training 65 3.4 Summary 69 3.5 Discussion Questions 69 3.6 For Further Reading 69 Chapter 4: Foundation Models 71 4.1 Foundation Models 71 4.2 Transformer Architecture 72 4.3 Alternatives in FM Architectures 74 4.4 Customizing FMs 75 4.5 Designing a System Using FMs 86 4.6 Maturity of FMs and Organizations 91 4.7 Challenges of FMs 93 4.8 Summary 94 4.9 Discussion Questions 94 4.10 For Further Reading 94 Chapter 5: AI Model Life Cycle 97 5.1 Developing the Model 97 5.2 Building the Model 108 5.3 Testing the Model 109 5.4 Release 114 5.5 Summary 114 5.6 Discussion Questions 115 5.7 For Further Reading 115 Chapter 6: System Life Cycle 117 6.1 Design 118 6.2 Developing Non-AI Modules 121 6.3 Build 122 6.4 Test 123 6.5 Release and Deploy 125 6.6 Operate, Monitor, and Analyze 135 6.7 Summary 140 6.8 Discussion Questions 141 6.9 For Further Reading 141 Chapter 7: Reliability 143 7.1 Fundamental Concepts 143 7.2 Preventing Faults 145 7.3 Detecting Faults 149 7.4 Recovering from Faults 152 7.5 Summary 154 7.6 Discussion Questions 154 7.7 For Further Reading 154 Chapter 8: Performance 155 8.1 Efficiency 155 8.2 Accuracy 164 8.3 Summary 173 8.4 Discussion Questions 173 8.5 For Further Reading 174 Chapter 9: Security 175 9.1 Fundamental Concepts 176 9.2 Approaches to Mitigating Security Concerns 180 9.3 Summary 188 9.4 Discussion Questions 189 9.5 For Further Reading 189 Chapter 10: Privacy and Fairness 191 10.1 Privacy in AI Systems 192 10.2 Fairness in AI Systems 193 10.3 Achieving Privacy 194 10.4 Achieving Fairness 197 10.5 Summary 201 10.6 Discussion Questions 201 10.7 For Further Reading 202 Chapter 11: Observability 203 11.1 Fundamental Concepts 203 11.2 Evolving from Monitorability to Observability 204 11.3 Approaches for Enhancing Observability 207 11.4 Summary 211 11.5 Discussion Questions 211 11.6 For Further Reading 212 Chapter 12: The Fraunhofer Case Study: Using a Pretrained Language Model for Tendering 213 12.1 The Problem Context 214 12.2 Case Study Description and Setup 217 12.3 Summary 232 12.4 Takeaways 233 12.5 Discussion Questions 233 12.6 For Further Reading 233 Chapter 13: The ARM Hub Case Study: Chatbots for Small and Medium-Size Australian Enterprises 235 13.1 Introduction 235 13.2 Our Approach 236 13.3 LLMs in SME Manufacturing 238 13.4 A RAG-Based Chatbot for SME Manufacturing 238 13.5 Architecture of the ARM Hub Chatbot 239 13.6 MLOps in ARM Hub 244 13.7 Ongoing Work 251 13.8 Summary 252 13.9 Takeaways 253 13.10 Discussion Questions 254 13.11 For Further Reading 254 Chapter 14: The Banking Case Study: Predicting Customer Churn in Banks 255 14.1 Customer Churn Prediction 256 14.2 Key Challenges in the Banking Sector 265 14.3 Summary 265 14.4 Takeaways 266 14.5 Discussion Questions 266 14.6 For Further Reading 267 Chapter 15: The Future of AI Engineering 269 15.1 The Shift to DevOps 2.0 270 15.2 AI's Implications for the Future 271 15.3 AIWare or AI-as-Software 276 15.4 Trust in AI and the Role of Human Engineers 279 15.5 Summary 280 15.6 Discussion Questions 281 15.7 For Further Reading 281 References 283 Index 289

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