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Architecting AI Software Systems

You're reading from Architecting AI Software Systems Crafting robust and scalable AI systems for modern software development

Product type Paperback

Published in Oct 2025

Last Updated in Oct 2025

Publisher Packt

ISBN-13 9781804615973

Length 212 pages

Edition 1st Edition

Concepts

Software Architecture

Authors (2):

Richard D Avila

Imran Ahmad

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Table of Contents (14) Chapters

Preface

1. Architecting Fundamentals

2. Fundamentals of AI System Architecture FREE CHAPTER

3. The Case for Architecture

4. Software Engineering and Architecture

5. Architecting AI Systems

6. Conceptual Design for AI Systems

7. Requirements and Architecture for AI Pipelines

8. Design, Integration, and Testing

9. Architecting a Generative AI System – A Case Study

10. Insights and Future Directions

11. Unlock Your Book’s Exclusive Benefits

How to unlock these benefits in three easy steps

12. Other Books You May Enjoy

13. Index

References

Kreuzberger, D., Kühl, N., & Hirschl, S. (2022). Machine Learning Operations (MLOps): Overview, Definition, and Architecture. IEEE Access, 10, 66631-66648.
Mäkinen, S., Skogström, H., Laaksonen, E., & Mikkonen, T. (2021). Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help? IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI, 109-112.
Kästner, C., & Kang, E. (2020). Teaching Software Engineering for AI-Enabled Systems. ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering Education and Training, 45-48.

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Authors (2)

Richard D Avila

Richard D Avila

Mr. Avila is a software and systems architect with over two decades of industry experience building complex software systems. He has architected and held leadership roles for building a wide array of software systems from complex simulations, autonomy and data analytic systems. He is also a principal investigator for AI topics. He has published in referred journals and industry publications on command-and-control theory, assurance architectures, multi-agent modeling, and machine learning. He was the first expert instructor on data analytics at University of Maryland Baltimore County – Training Centers. Before working as a software and systems architect, he served in the US Navy as a submarine officer. He resides in Annapolis, Maryland USA.

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Imran Ahmad

Imran Ahmad

Imran Ahmad is a certified Google Instructor who has been teaching for Google and Learning Tree for the last many years. Imran completed his PhD in 2009 in which he proposed an AI used to develop intelligent clouds for Sudbury Neutrino Observatory. Since 2005 Imran is working in a high-profile machine learning project at the Advanced Analytics Lab of the Canadian Federal Government. The project is to develop machine learning algorithms that can automate the process of immigration. Currently Imran is also doing research on semi-supervised learning and edge computing

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