System Architecture defines the holistic structure integrating hardware, software, data infrastructure, and operational components to deliver capabilities at scale, encompassing distributed training infrastructure, inference servers, data pipelines, model registries, monitoring systems, and edge-cloud coordination.
Semantic Classification
Content
Key Characteristics
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Integrates compute, storage, and networking infrastructure
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Supports distributed training and inference at scale
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Implements fault tolerance and disaster recovery mechanisms
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Optimizes resource utilization and cost efficiency
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Enables multi-region deployment and data sovereignty compliance
Overview
System Architecture in AI defines the holistic structure integrating hardware, software, data infrastructure, and operational components to deliver intelligent capabilities at scale. This encompasses distributed training infrastructure, inference servers, data pipelines, model registries, monitoring systems, and edge-cloud coordination. Architectural decisions address latency requirements, throughput targets, fault tolerance, security, and cost optimization. Modern AI systems adopt cloud-native, containerized architectures with orchestration platforms (Kubernetes), enabling elastic scaling, multi-tenancy, and global deployment.
Related Concepts
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References
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Dean, J. (2020). The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design. ISSCC 2020 Keynote.
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Zaharia, M. et al. (2018). Accelerating the Machine Learning Lifecycle with MLflow. IEEE Data Engineering Bulletin, 41(4), 39-45.
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Burns, B. et al. (2016). Borg, Omega, and Kubernetes. ACM Queue, 14(1), 70-93.