March 2025
Intermediate to advanced
320 pages
8h 31m
English
book
Engineering AI Systems: Architecture and DevOps Essentials
by Len Bass, Qinghua Lu, Ingo Weber, Liming Zhu
Content preview from Engineering AI Systems: Architecture and DevOps Essentials
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4
Foundation Models
For data guzzling AI companies, the internet is too small.
—Wall Street Journal
IN CHAPTER 1, Introduction, we differentiated between traditional machine learning (ML) models, which are trained for narrow tasks, and foundation models (FMs), which are trained for general purposes through the synthesis of massive amounts of data such as natural language, code, images, and videos. In Chapter 3, AI Background, we explored ML models in more detail. In this chapter, we go into detail on FMs.
We discuss FMs in general, the transformer architecture that underlies their strong advances as well as some alternatives, and how you customize FMs. Then we explore the design considerations when you are building a system using FMs, the maturity ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.