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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

Summary

Architecting AI pipelines requires careful coordination between data management, model development, infrastructure design, and software integration practices. The comprehensive pipeline architecture involves parallel development pipelines (for model creation and testing) and production pipelines (for deployment and value delivery), connected through well-defined transition processes.

Key considerations for successful implementation include data-centric design focusing on stores with appropriate characteristics for specific workloads, modular architecture with well-defined and independently verifiable components, quality assurance through comprehensive data checks and transformations, monitoring and observability across all pipeline components, DevOps integration enabling rapid iteration and controlled deployment, non-functional requirements driving architectural decisions beyond basic functionality, and governance and compliance frameworks embedded throughout both pipeline...

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