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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.
We wrote this book to help you whether you know about AI or Software Engineering. We take the approach that engineering an AI system is an extension of engineering a non-AI system with some special characteristics. That is, it involves using modern software engineering techniques and integrating them with the development of an AI model trained with an appropriate set of data. We highlight new technologies like foundation models. Each chapter ends with a set of discussion questions so that you and your colleagues can further discuss the issues raised by the chapters and so that you all are on the same page. One of the problems with multidisciplinary teams is vocabulary. Words may have different meanings depending on your background. Discussing each chapter with your colleagues will also help resolve and agree on the meanings of words.
Our approach is that there are three contributors to the building of high-quality systems – 1) 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 on.
Introduction
Software Engineering Background
AI Background
Foundation Models
AI Model Lifecycle
System Lifecycle
Reliability
Performance
Security
Privacy and Fairness
Observability
The Fraunhofer Case Study: Using a Pretrained Language Model for Tendering
The ARM Hub Case Study: Chatbots for Small and Medium Size Australian Enterprises
The Banking Case Study: Predicting Customer Churn in Banks
The Future of AI Engineering
References