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Machine Learning on VMware vSphere: Workload Types and When GPU Usage Make Sense

Start Date:8/14/2019

Start Time:11:00 AM PDT

Duration:60 minutes


Now Available On Demand
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.

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Justin Murray

Senior Technical Marketing Architect, Cloud Platform Business Unit


Justin is an architect within the Technical Marketing for the vSphere team at VMware. His specializations are in the areas of machine learning, deep learning and big data for application use. He writes technical articles on these subjects for customers to use in setting up their VMware environments so as to maximize their benefits from it. This involves working closely with the leading tools and platform vendors (ISVs) in these areas to ensure their products fit well with the VMware platform and derive manageability and performance benefits from it. Justin works on both on-premise vSphere and on the VMware Cloud on AWS public cloud service.

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