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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

User interaction patterns

While the technical architecture provides the foundation, the ultimate value of TechSolve’s system is realized through its interactions with users. This section explores typical workflows and interaction patterns that demonstrate how support engineers and other stakeholders engage with the system to address customer needs efficiently.

To illustrate how different stakeholders interact with the system, TechSolve’s architects documented key usage scenarios that capture common workflows.

Use case: Query resolution

This primary workflow represents the most common interaction pattern with TechSolve’s knowledge system. When faced with a complex customer issue, support engineers leverage the AI system to rapidly access relevant information across organizational knowledge repositories, enabling faster and more accurate resolutions than previously possible through manual searches.

Primary actor: Support engineer

Secondary actor...

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