Additional information
by Richard D. Avila (Author), Imran Ahmad (Author)
Discover how to design intelligent software systems by balancing AI and traditional architecture. This guide offers a roadmap for robust, scalable AI-enabled systems, blending principles with practical insights.
Key Features:
- Learn to integrate AI with traditional software architectures, enabling architects to design scalable, high-performance systems
- Explore key tools and processes to mitigate risks in AI-driven system development, ensuring timely project delivery and budget control
- Gain hands-on experience through case studies and exercises, applying architectural concepts to real-world AI systems
Book Description:
Architecting AI Software Systems provides a definitive guide to building AI-enabled systems, emphasizing the balance between AI's capabilities and traditional software architecture principles.
As AI technologies gain widespread acceptance and are increasingly expected in future applications, this book provides architects and developers with the essential knowledge to stay competitive. It introduces a structured approach to mastering the complexities of AI integration, covering key architectural concepts and processes critical to building scalable and robust AI systems while minimizing development and maintenance risks. The book guides readers on a progressive journey, using real-world examples and hands-on exercises to deepen comprehension. It also includes the architecture of a fictional AI-enabled system as a learning tool. You will engage with exercises designed to reinforce your understanding and apply practical insights, leading to the development of key architectural products that support AI systems. This is an essential resource for architects seeking to mitigate risks and master the complexities of AI-enabled system development.
By the end of the book, readers will be equipped with patterns, strategies and concepts necessary to architect AI-enabled systems across various domains.
What You Will Learn:
- Understand the challenges of building AI-enabled systems and managing risks like underperformance and cost overruns
- Learn architectural tools to design and integrate AI into traditional systems
- Master AI/ML concepts like inference and decision-making and their impact on architecture
- Use architectural models to ensure system cohesion and functionality
- Simulate and optimize AI performance through prototyping and iteration
- Design scalable AI systems using patterns and heuristics
- Integrate AI into large systems with a focus on user experience and performance
Who this book is for:
This book is designed mainly for software and systems architects responsible for designing and integrating AI capabilities into existing and new systems. It also serves as a valuable resource for CTOs, VPs of Engineering, and aspiring architects seeking a comprehensive understanding of the holistic approach to AI system development. Additionally, AI/ML engineers and software developers will benefit from gaining deeper insights into the architectural principles that underpin AI systems, enabling them to align their work with broader architectural goals
Table of Contents
- Challenges of building AI enabled systems
- The case for architecture
- Software engineering and architecture
- Conceptual Design
- Requirements and Architecture for AI Pipelines
- Design, Integration and Test
- Architecting a Generative AI System - A Case Study
- Insights and Future Directions
