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Table Of Contents
Architecting AI Software Systems
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Architecting AI pipelines requires careful coordination between data management, model development, infrastructure design, and software integration practices. The comprehensive pipeline architecture involves parallel development pipelines (for model creation and testing) and production pipelines (for deployment and value delivery), connected through well-defined transition processes.
Key considerations for successful implementation include data-centric design focusing on stores with appropriate characteristics for specific workloads, modular architecture with well-defined and independently verifiable components, quality assurance through comprehensive data checks and transformations, monitoring and observability across all pipeline components, DevOps integration enabling rapid iteration and controlled deployment, non-functional requirements driving architectural decisions beyond basic functionality, and governance and compliance frameworks embedded throughout both pipeline...
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