Название: Engineering AI Systems: Architecture and DevOps Essentials (Final Release)
Автор: Len Bass, Qinghua Lu, Ingo Weber, Liming Zhu
Издательство: Addison-Wesley Professional/Pearson Education
Год: 2025
Страниц: 347
Язык: английский
Формат: pdf, epub
Размер: 11.0 MB

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.

We suggest that there are three contributors to the building of high-quality systems: (1) the software architecture; (2) the processes used for building, testing, deployment, and operations (DevOps); and (3) high-quality AI models and the data on which they depend. All three areas have been evolving over the last 20-plus years. Their evolution has, for the most part, been carried out by communities that are largely independent of each other, that all have their own approaches to constructing a high-quality system, and that emphasize different aspects of a quality system. These differences are manifested in different emphases on factors of quality. For example, software engineers interpret performance primarily as efficiency, AI specialists interpret performance as primarily the accuracy of results, and DevOps specialists interpret performance as the speed with which a system moves through the deployment pipeline.

Building large AI-based systems is difficult and involves understanding multiple technologies. Our goal is to assist you in your journey to understand these technologies, so that you can create better AI systems that meet your goals and the goals of your stakeholders.

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.