Storage-Optimized Machine Learning


Organizations run at the speed of their data. While Artificial Intelligence (AI) and Machine Learning (ML) have a continuing history of solving traditional problems in pattern recognition, AI and ML techniques are rapidly finding their place in business analytics, where the patterns being determined might be less obvious. The efficiency of these learning systems can define an organization’s competitive advantage.

Machine Learning has long been implemented on top of traditional compute architectures, where throughput and latencies are determined by coupling compute and storage through the same networking and storage interconnects that serve other business applications. The increasing volume and velocity of arriving data are stressing these architectures, whether for real-time processing of Internet-of-Things (IoT) telemetry, pattern recognition in images or audio, or mining data from the warehouse to gain new insights about an organization’s customers or business.

Moor Insights & Strategy

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