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Table Of Contents
Architecting AI Software Systems
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Machine learning model development fundamentally differs from traditional software engineering in its experimental and iterative nature. While software engineers typically design systems based on well-defined specifications, data scientists must navigate the inherent uncertainties of data characteristics, feature relevance, and model behavior. This necessitates a systematic yet flexible approach to model creation, optimization, and validation that accommodates the unique challenges of AI development.
This chapter examines “AI pipeline systems” – the predominant architecture in enterprise AI today, which consist of progressive processing stages utilizing interconnected AI models. These pipelines form the backbone of modern AI implementations, enabling organizations to systematically develop, deploy, and maintain AI capabilities at scale.
AI systems rarely operate as standalone modules; rather, they are typically...
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