Victor Thu is the General Manager of Neurobots at Petuum, partnering closely with Bin Zhao, to build easy-to-use cutting-edge AI/ML solutions that integrate seamlessly into RPA as IPA (intelligent process automation). Before Petuum, Victor led marketing and product marketing for Digitate, a startup that focuses on solving IT operational challenges using AI and automation. As an industry technologist, Victor held different roles leading go-to-market strategies for innovative technologies, including the converged desktop, mobility, and identity solutions at VMware. Victor was an industry subject matter expert on business and workforce mobility with topics such as BYOD (Bring Your Own Devices). Victor also spent over three years in the Asia Pacific region, leading end-user computing product marketing for VMware and Citrix across the region.
Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, Machine Learning, and cloud computing. Over the past few years, they have worked with some of the World's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World's most popular soft drinks companies, helping each of them to better make sense of its data, and process it in more intelligent ways. Jim DiLorenzo is a freelance programmer and reinforcement learning enthusiast. He graduated from Columbia University and is working on his Masters in Computer Science.
Update: As of August 2019, the Bitfusion acquisition has closed. We would like to welcome Bitfusion and its staff to VMware. Increasingly businesses are applying artificial intelligence (AI) technologies to differentiate and advance their processes and offerings. Today, I am pleased to announce our intent to acquire Bitfusion to help businesses more efficiently use AI technologies on-premises and in hybrid cloud environments. Hardware acceleration for applications--which can take the form of GPUs (graphics processor units), FPGAs (field-programmable gate arrays), and ASICs (application-specific integrated circuits)--delivers efficiency and flexibility into the AI space including subsets such as machine learning.
VMware is developing a cloud service to monitor software in customer deployments and tune it automatically to improve performance. This is Project Magna and its first target is vSAN in hyperconverged infrastructure. It will work like this: customers select their key performance indicator – read or write optimisation or both. Magna examines their vSAN environment and compares it to the KPI average for stored and monitored deployments. If the site is below average, Magna changes it to bring it closer to the average.
Get a look at the future of virtualization with NVIDIA's latest demos -- from accelerated AI to RTX graphics -- at VMworld, this week in San Francisco. Thousands of technical professionals, software architects, data scientists and more will be at the annual conference to gain insight into the most recent advancements in virtualization. NVIDIA vComputeServer brings GPU acceleration to server workloads like AI, deep learning and high performance computing. Businesses can run GPU workloads in virtualized environments for improved security, utilization and manageability. And with NVIDIA NGC GPU-optimized software, users can access models, training scripts and workflows that can further accelerate AI.
SAN FRANCISCO, California--VMware announced today that it has closed its Uhana acquisition, and unveiled the next release of its OpenStack solution. Uhana, which provides artificial intelligence and machine learning optimization for mobile networks, will be particularly useful in 5G radio access networks (RANs) and the buildout of the 5G core. "We see a major change coming with all the radio aspects," said VMware's Gabriele di Piazza, vice president of managed products and solutions for telco NFV and edge cloud. "We invested in Uhana to provide fine-grained streaming analytics, which actually leverage direct feeds from radio networks, and then we can apply AI techniques, such as machine learning, to what we call observe, predict and control your network. "With Uhana, we are getting deep into the network.
NVIDIA's virtual GPU (vGPU) technology, which has already transformed virtual client computing, now supports server virtualization for AI, deep learning and data science. Previously limited to CPU-only, AI workloads can now be easily deployed on virtualized environments like VMware vSphere with new vComputeServer software and NVIDIA NGC. Through our partnership with VMware, this architecture will help organizations to seamlessly migrate AI workloads on GPUs between customer data centers and VMware Cloud on AWS. IT administrators can use hypervisor virtualization tools like VMware vSphere, including vCenter and vMotion, to manage all their data center applications, including AI applications running on NVIDIA GPUs. These GPU servers are often isolated, with the need to be managed separately.
Today Mellanox announced that its RDMA (Remote Direct Memory Access) networking solutions for VMware vSphere enable virtualized Machine Learning solutions that achieve higher GPU utilization and efficiency. The benchmark was performed on a four-node cluster running vSphere 6.7 equipped with NVIDIA T4 GPUs with vCS software and Mellanox ConnectX-5 100 GbE SmartNICs, all connected by a Mellanox Spectrum SN2700 100 GbE switch. The PVRDMA Ethernet solution enables VM-to-VM communication over RDMA, which boosts data communication performance in virtualized environments while achieving significantly higher efficiency compared with legacy TCP/IP transports. Additionally, PVRDMA retains core virtual machine capabilities such as vMotion. This translates to real-world customer advantages including optimized server and GPU utilization, reduced machine learning training time and improved scalability.
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.