迈向统一智能基础设施：整合人工智能、云计算和数据中心实现可扩展和自主运营.pdf

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1、Ankur Sharma Principal Architect(Equinix)Ahmad Byagowi Research Scientist(Meta)AI at Scale Needs Intelligent InfrastructureFrom Training to Inference:Sovereignty,Observability,and Cloud-Native AgilityAI at Scale Needs Intelligent InfrastructureFrom Training to Inference:Sovereignty,Observability,and

2、 Cloud-Native AgilityAnkur Sharma Principal Architect(Equinix)Ahmad Byagowi Research Scientist(Meta)Artificial Intelligence(Special Focus)Why Now?AI training drives massive scale;inference demands low latency and localityCloud-native expectation:Speed,Elasticity,API-driven controlData centers need u

3、nified observability to manage complexity at scaleIntegration is no longer optional-its the key to agility and resilienceAI,Hybrid-Multi-Cloud,Observability,and Sovereignty are converging faster than everSovereignty and Compliance are now core design ComponentsTodays infrastructure operates in silos

4、Disconnected telemetry and observability tools across hardware,network,and applicationsManual,reactive workload placement and scaling across clouds and data centersBlind spots in AI workload performance,resource utilization,and localityLatency and bandwidth constraints affecting both AI inference(lo

5、w-latency)and training(scale-intensive)workloadsCompliance and sovereignty gaps:Infrastructure not designed for jurisdiction-aware policiesThe ProblemA tightly integrated ecosystem with built-in observability and sovereigntyAI-driven optimization:closed-loop decisioning from telemetry to actionUnifi

6、ed observability:correlate logs,metrics,and traces across layers in real timeCloud-native scalability:elastic orchestration across environmentsData center automation:fabric+optical interconnect as first-class citizensSelf-optimizing systems:from silicon to orchestration layerThe Vision:Unified Intel