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Architecting AI Software Systems

Architecting AI Software Systems

By : Richard D Avila, Imran Ahmad
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Architecting AI Software Systems

Architecting AI Software Systems

5 (2)
By: Richard D Avila, Imran Ahmad

Overview of this book

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.
Table of Contents (14 chapters)
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1
Architecting Fundamentals
5
Architecting AI Systems
12
Other Books You May Enjoy
13
Index

Understanding software complexity in AI systems

Several distinct but interdependent types of software complexity exist in AI-enabled systems. Architecture serves as a tool to manage this complexity through modeling, which helps the architecture team reason about the system and provides a foundation for detailed specifications and team communication.

Figure 3.1: Software Development Lifecycle for AI Systems

As shown in Figure 3.1, the development process for AI systems follows a structured lifecycle that begins with conceptual development, then moves through requirements gathering and architecture development before proceeding to design and development. After development, the process continues with integration, unit testing, system testing, and finally deployment. This systematic approach helps manage the inherent complexities of AI systems by ensuring that architectural considerations are addressed early in the development process. An excellent resource for learning about systems methodology...

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