Ingo Weber, Len Bass, Liming Zhu, Qinghua Lu

Engineering AI Systems

Architecture and DevOps Essentials

• Broschiertes Buch

Produktbeschreibung

Transform Your Business with AI: The Ultimate Guide to Engineering AI Systems In the rapidly evolving world of business, integrating artificial intelligence (AI) into your systems is no longer optional. Engineering AI Systems: Architecture and DevOps Essentials is a comprehensive guide that will help you master 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 intricate process of engineering AI systems, providing practical strategies and tools for seamlessly incorporating AI into your business operations. You will gain a comprehensive understanding of the fundamentals of AI and software engineering and how they intersect 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 strategic insights into designing AI systems to align with your business goals. Lifecycle management of AI models, from data preparation to deploymentBest practices in system architecture and DevOps for AI systemsSystem reliability, performance, and security in AI implementationsPrivacy and fairness in AI systems to build trust and complianceTechniques for monitoring and observing AI systems to maintain operational excellenceFuture trends in AI engineering to stay ahead of the curve Equip yourself with the knowledge and tools to transform your business with AI. 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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Produktdetails

• Verlag: Pearson Education (US)
• Seitenzahl: 304
• Erscheinungstermin: 8. Juni 2025
• Englisch
• ISBN-13: 9780138261412
• ISBN-10: 0138261415
• Artikelnr.: 71954670

Autorenporträt

Dr. Len Bass is a seasoned researcher with over 30 years in software architecture and more than a decade in DevOps. He has been teaching DevOps to graduate students for seven years and is the author of a bestselling book on software architecture, along with three books on DevOps. Dr. Qinghua Lu is a principal research scientist at CSIRO's Data61, leading the Software Engineering for AI and Responsible AI science teams. She is a coauthor of Responsible AI: Best Practices for Creating Trustworthy AI Systems (Addison-Wesley, 2024). Prof. Dr. Ingo Weber is a professor at the Technical University of Munich and Director of Digital Transformation and ICT Infrastructure at Fraunhofer-Gesellschaft. He has written numerous publications and textbooks, including DevOps: A Software Architects Perspective and Architecture for Blockchain Applications. Dr. Liming Zhu is a research director at CSIRO's Data61 and is a conjoint professor at University of New South Wales. He contributes to various AI safety and standards committees and has written over 300 papers. He is coauthor of Responsible AI: Best Practices for Creating Trustworthy AI Systems.

Inhaltsangabe

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