It's easy for your business to get started with ML by analyzing tabular data using existing CPU-based VMs – no specialized hardware required. We explore a range of applications for ML using VMware Cloud on AWS that can deliver immediate value. This series of blog articles presents different use cases for deploying machine learning algorithms and applications on VMware Cloud on AWS and other VMware Cloud infrastructure. At the time of writing, June 2020, the hardware accelerators for neural networks are not yet available on VMware Cloud on AWS. However, there are many very good reasons to deploy classic machine learning algorithms that perform well on CPU-based VMs onto VMware Cloud on AWS. We describe these use-cases in this and the following articles.

NVIDIA and VMware today announced their intent to deliver accelerated GPU services for VMware Cloud on AWS to power modern enterprise applications, including AI, machine learning and data analytics workflows. These services will enable customers to seamlessly migrate VMware vSphere-based applications and containers to the cloud, unchanged, where they can be modernized to take advantage of high-performance computing, machine learning, data analytics and video processing applications. Increasingly businesses are applying artificial intelligence (AI) technologies to differentiate and advance their processes and offerings. Enterprises are rapidly adopting AI(1) and implementing new AI strategies that require powerful computers to create predictive models from petabytes of corporate data. Across industries, enterprises are implementing machine learning applications such as image and voice recognition, advanced financial modeling and natural language processing using neural networks that rely on NVIDIA GPUs for faster training and real-time inference.

In July, VMware acquired Bitfusion, a company whose technology virtualizes compute accelerators with the goal of enabling modern workloads like artificial intelligence and data analytics to take full advantage of systems with GPUs or with FPGAs. Specifically, Bitfusion's software allows for virtual machines to offload compute duties to GPUs, FPGAs, or even other kinds of ASICs. The deal didn't get a ton of attention at the time, but for VMware, it was an important step in realizing its cloud ambitions. "Hardware acceleration for applications delivers efficiency and flexibility into the AI space, including subsets such as machine learning," Krish Prasad, senior vice president and general manager of VMware's Cloud Platform business unit, wrote in a blog post announcing the acquisition. "Unfortunately, hardware accelerators today are deployed with bare-metal practices, which force poor utilization, poor efficiencies, and limit organizations from sharing, abstracting and automating the infrastructure. This provides a perfect opportunity to virtualize them – providing increased sharing of resources and lowering costs."

According to a new press release, "NVIDIA and VMware today announced their intent to deliver accelerated GPU services for VMware Cloud on AWS to power modern enterprise applications, including AI, machine learning and data analytics workflows. These services will enable customers to seamlessly migrate VMware vSphere-based applications and containers to the cloud, unchanged, where they can be modernized to take advantage of high-performance computing, machine learning, data analytics and video processing applications." The release goes on, "Increasingly businesses are applying artificial intelligence (AI) technologies to differentiate and advance their processes and offerings. Enterprises are rapidly adopting AI1 and implementing new AI strategies that require powerful computers to create predictive models from petabytes of corporate data. Across industries, enterprises are implementing machine learning applications such as image and voice recognition, advanced financial modeling and natural language processing using neural networks that rely on NVIDIA GPUs for faster training and real-time inference. Additionally, VMware recently acquired Bitfusion, which enables VMware to efficiently make GPU capabilities available for AI and machine learning workloads in the enterprise."

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VMworld--NVIDIA and VMware today announced their intent to deliver accelerated GPU services for VMware Cloud on AWS to power modern enterprise applications, including AI, machine learning and data analytics workflows. These services will enable customers to seamlessly migrate VMware vSphere-based applications and containers to the cloud, unchanged, where they can be modernized to take advantage of high-performance computing, machine learning, data analytics and video processing applications. Increasingly businesses are applying artificial intelligence (AI) technologies to differentiate and advance their processes and offerings. Enterprises are rapidly adopting AI(1) and implementing new AI strategies that require powerful computers to create predictive models from petabytes of corporate data. Across industries, enterprises are implementing machine learning applications such as image and voice recognition, advanced financial modeling and natural language processing using neural networks that rely on NVIDIA GPUs for faster training and real-time inference.

Nvidia and VMware on Monday announced a new software product that lets customers virtualize GPUs, either on premise or as part of VMware Cloud on AWS. The companies say it's the first hybrid cloud offering that lets enterprises use GPUs to accelerate AI, machine learning or deep learning workloads. "In a modern data center, organizations are going to be using GPUs to power AI, deep learning, analytics," John Fanelli, VP of product management for Nvidia, told reporters. "And due to the scale of those types of workloads, they're going to be doing some processing on premise in data centers, some processing in clouds and continually iterating between them." The new offering starts with the enterprise data center product -- Nvidia's new Virtual Compute Server (vComputeServer) software.

Nvidia and VMware today announced the launch of an accelerated GPU service on VMware Cloud on AWS, forming an advanced hybrid cloud infrastructure for machine learning workloads. The new service will allow organisations to migrate VMware vSphere-based applications and containers to VMware Cloud on AWS, VMware's cloud service that runs on bare metal infrastructure in AWS data centres, where they can take advantage of high-performance computing, machine learning, data analytics and video processing applications, backed up by Nvidia accelerators. Specifically, VMware Cloud on AWS customers will be able to rent Amazon EC2 bare metal instances, an AWS service that provides resizable compute capacity, that are accelerated by Nvidia T4 100 GPUs. The lynchpin of the hybrid platform is Nvidia's vComputeServer, virtual vGPU technology that enables GPU-accelerated deployment of workloads in virtual environments. The technology is not technically new but Nvidia has expanded support to VMware virtual environments including vSphere, vCenter and VMware cloud.

If you've ever found yourself wishing you could do all the things you've been able to do with a hypervisor and regular virtual machines but on a GPU cluster – in your own data center or in the cloud – Nvidia and VMware are now saying your wish is about to come true. Monday morning, in conjunction with the start of VMworld in San Francisco, the two companies announced that VMware Cloud on AWS, the VMware-operated cloud service running on bare-metal infrastructure in AWS data centers, will soon feature virtualized GPUs you'll be able to provision and manage using the same vSphere tools you use with regular VM infrastructure. You'll be able to share a single physical GPU among multiple VMs, but you'll also be able to aggregate the power of many GPUs to train a machine-learning model at massive scale, the companies said. Related: VMworld: Look at Acquisitions for Virtualization's Cloud Play The play here is to get VMware into the infrastructure mix for the emerging set of enterprise computing workloads that benefit from GPU acceleration, such as AI and machine learning, as well as more traditional Big Data analytics. Also on Monday, the company announced a broad strategy for tackling the hybrid cloud opportunity, which is essentially to provide a single set of tools for managing all enterprise infrastructure, on premises and/or in any public cloud, in a uniform way.

Nvidia and VMware today announced an enterprise-grade hybrid cloud on AWS that is optimized for machine learning, AI, and data science workflows. The VMware Cloud on AWS with Nvidia is capable of operating from cloud and on-premise servers and will make it easier to migrate VMware vSphere-based applications to the cloud to accelerate high-performance computing or machine learning for research, experimentation, and deployment in production. Today's news comes at the beginning of the VMworld conference in San Francisco and just days after VMware acquired cloud security and app development startups Pivotal and Carbon Black for $5 billion. Nvidia today also introduced vCompute Server for the GPU-accelerated deployment of workloads in virtual environments, including VMware's vSphere, vCenter, and VMware Cloud. The latest virtual GPU offering from Nvidia can make the completion of deep learning training up to 50 times faster than with a CPU alone.