Engineering AI Systems: DevOps and Architecture Approaches, 1st edition

Master the engineering of AI Systems

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 organisation'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.