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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

Data engineering

A principal insight in this new era of data analytics and science is that a system’s effectiveness is dependent on the quality of the data. This truism applies to the training and configuring of models, inference, the validation processes, and the production data. This has led to the emergence of a new engineering sub-specialty: the data engineer.

The data engineer must deal with the volume, velocity, variety, and provenance of the total data ecosystem. The field of data engineering is still in its early stages. These activities are now recognized as an engineering sub-specialty rather than “just data processing.” When one engineers a system, the concepts and processes of architecture come to the fore. The data engineering efforts require the scoping of the data volumes, processing needs, storage capacity, and networking infrastructure. These analyses impact the planning of required hardware and software tooling.

As part of the architecting...

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