AI and ML Architectures: Fundamentals & Practical Considerations

Artificial Intelligence (AI) and Machine Learning (ML) architectures represent a generational shift in how high-performance computing environments are deployed. The process of architecting AI and ML clusters requires specific network infrastructure centered on low-latency and high-bandwidth links to ensure not only optimal network performance but also streamlined deployment timelines.

Fundamental to this architectural change is the profound influence of parallel processing capabilities, serving as the driving force behind the learning tasks that form the core of AI’s value proposition. Notably, AI clusters are transitioning from traditional CPU-based hardware to GPU-based nodes. This shift not only signifies a change in computing paradigms but also presents a substantial challenge to established data center architectures, challenging current network design paradigms.

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AI and ML Architectures: Fundamentals & Practical Considerations

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