BrandPost: Enabling the convergence of AI, HPC and data analytics

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The convergence of artificial intelligence, high performance computing and data analytics is being driven by a proliferation of advanced computing workflows that combine different techniques to solve complex problems.Here’s one example: AI and data analytics can augment traditional HPC workloads to speed scientific discovery and innovation. At the same time, data scientists and researchers are developing new processes for solving problems at massive scale that require HPC systems and oftentimes AI-driven applications like machine learning and deep learning.While this convergence is accelerating discovery and innovation, it’s also putting pressure on IT shops to support increasingly complex environments. IT teams are being asked to complete manual configurations and reconfigurations of servers, storage and networking as they move nodes between clusters to provide the resources required for shifting workload demands.To read this article in full, please click here

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The convergence of artificial intelligence, high performance computing and data analytics is being driven by a proliferation of advanced computing workflows that combine different techniques to solve complex problems.

Here’s one example: AI and data analytics can augment traditional HPC workloads to speed scientific discovery and innovation. At the same time, data scientists and researchers are developing new processes for solving problems at massive scale that require HPC systems and oftentimes AI-driven applications like machine learning and deep learning.

While this convergence is accelerating discovery and innovation, it’s also putting pressure on IT shops to support increasingly complex environments. IT teams are being asked to complete manual configurations and reconfigurations of servers, storage and networking as they move nodes between clusters to provide the resources required for shifting workload demands.

To read this article in full, please click here

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