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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

Insights and Future Directions

We have made the case for how to build AI-enabled systems. Building AI-enabled systems is a challenge. There exist high expectations for the system and the likelihood that the software system is complex. There are several ways for a software development effort to fail; these range from not understanding key requirements, a technology not performing as expected, an erroneous system design, and compute, storage, or data flows being misunderstood. A major driver for failure is that humans can lose trust in the system if outputs are inconsistent, wrong, or just not sensible. AI-enabled systems have these risks plus algorithmic complexity, sensitive to off-nominal scenarios, and data inputs being off. A key driver of AI system failures is that users lose trust or have low confidence in the results provided by the system. AI-enabled systems must be built from inception so that they will utilize AI technologies. These systems must address decision-making without...

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