Software Architecture for AI systems defines high-level structural patterns, component interactions, and design principles for building scalable, maintainable, and robust artificial intelligence applications. It encompasses microservices decomposition, event-driven designs, lambda and kappa architectures, feature stores, model registries, and observability pipelines, balancing modularity, reproducibility, and operational excellence.
Semantic Classification
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Key Characteristics
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Separates concerns into distinct architectural layers
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Supports horizontal scaling and load distribution
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Facilitates continuous deployment and rollback mechanisms
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Integrates monitoring, logging, and observability
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Enables model experimentation and version management
Overview
Software Architecture for AI systems defines high-level structural patterns, component interactions, and design principles for building scalable, maintainable, and robust artificial intelligence applications. Architectural patterns include microservices (decoupled AI components), serverless (event-driven inference), model-view-controller (MVC) for AI applications, and lambda architecture (batch and stream processing). AI-specific concerns include model versioning, A/B testing infrastructure, feature stores, model registries, and monitoring systems. Modern architectures emphasize modularity, reproducibility, and operational excellence.
Related Concepts
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References
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Vogels, W. (2006). Eventually Consistent. Communications of the ACM, 52(1), 40-44.
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Kleppmann, M. (2017). Designing Data-Intensive Applications. O’Reilly Media.
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Sculley, D. et al. (2015). Hidden Technical Debt in Machine Learning Systems. NeurIPS 2015.