Unifying Big Data And Machine Learning, Cisco Style

It doesn't take a machine learning algorithm to predict that server makers are trying to cash in on the machine learning revolution at the major nexus points on the global Internet. Many server makers rose to satisfy the unique demands of the initial dot-com buildout back in the 1990s, and a new crop of vendors as well as some incumbents are trying to engineer some differentiation into their platforms to appeal to the machine learning crowd. This is particularly true for servers that are used to train neural nets, which require lots of very beefy GPU accelerators, almost universally those from Nvidia, as well as a few hefty CPUs, lots of main memory and usually fast networking, too. Cramming this plus enough storage to be useful into a single node that is then clustered to scale out performance in a parallel fashion (like transitional HPC workloads) is a challenge. But the opportunity is large enough – and profitable enough – that after a bunch of customers started asking for a server that plugged into its Unified Computing System framework, could be managed by the UCS Manager stack, and integrate into the UCS network fabric.