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vSphere is an ideal software infrastructure for running Machine Learning (ML) workloads. It can run both GPU-based ML and non-GPU-based ML in virtual machines on various kinds of host servers. Many organizations are looking to GPUs to drive to performance and achieve results faster. You can leverage GPU performance without compromise when running on vSphere. However, if you are just getting started, you can use your current CPU-based machines and do some forms of ML on tabular data with that environment, while planning to use more sophisticated neural network-based forms of ML down the road.

Listen to this webcast to learn more. We look at running two types of ML workloads on vSphere. One ML type uses table-oriented data and another uses images, voice, or video data identifying the best opportunities for using GPUs to accelerate workloads.