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.

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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 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 compliance
Techniques for monitoring and observing AI systems to maintain operational excellence
Future 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.

Contenu

Preface
Acknowledgments

Chapter 1: Introduction
Chapter 2: Software Engineering Background
Chapter 3: AI Background
Chapter 4: Foundation Models
Chapter 5: AI Model Lifecycle
Chapter 6: System Lifecycle
Chapter 7: Reliability
Chapter 8: Performance
Chapter 9: Security
Chapter 10: Privacy and Fairness
Chapter 11: Observability
Chapter 12: The Fraunhofer Case Study: Using a Pretrained Language Model for Tendering
Chapter 13: The ARM Hub Case Study: Chatbots for Small and Medium Size Australian Enterprises
Chapter 14: The Banking Case Study: Predicting Customer Churn in Banks
Chapter 15: The Future of AI Engineering

References
Index