Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Architecting AI Software Systems
  • Table Of Contents Toc
Architecting AI Software Systems

Architecting AI Software Systems

By : Richard D Avila, Imran Ahmad
5 (2)
close
close
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)
close
close
1
Architecting Fundamentals
5
Architecting AI Systems
12
Other Books You May Enjoy
13
Index

Algorithmic development components

As has been stated, the building of an AI system has as its core the building of components that are expected to make decisions. The decisions that are to be made rely heavily or almost entirely on the data that comes into the system. A decision is only as good and valid as the data that was used. The next sections describe key tasks that can be used to ensure the highest quality of data enters the system. These tasks can be both tedious and challenging since, for the initial system development, a human is needed. That said, for further system development, these tasks can be done in an automated manner with checking and alerting.

Data quality checks

Understanding data quality is critical for effectively training, tuning, and maintaining AI pipelines. Quality checks should be rigorously configuration-controlled and tested, with minimum requirements explicitly specified. These include comprehensive assessments of data completeness to ensure...

Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Architecting AI Software Systems
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon