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.
The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build. Dell Technologies is rolling out a series of designs and systems that aim to speed up artificial intelligence deployments by using VMware's acquired Bitfusion technology. Two Dell EMC Ready Solutions are based on VMware Validated Designs to combine Dell EMC hardware with VMware Cloud Foundation and AI management Bitfusion tools in VMware vSphere 7. Dell Technologies said that its Dell Dell Technologies is claiming to be among the first IT companies to equip systems to run AI workloads within VMware environments. Ravi Pendekanti, senior vice president of product management and marketing for Dell Technologies server unit, said the new systems are designed to run AI anywhere and take advantage of underutilized GPUs. "GPU instances are being underutilized and that is holding back AI," said Pendekanti.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
Furyion Games used Unity Game Simulation to playtest its forthcoming shooter, 'Death Carnival' Unity's newest tool allows game developers to run cloud-based playtests at unprecedented speed and scale with machine learning. T0day, as part of the Google for Games Developer Summit, Unity Technologies announced Unity Game Simulation. It's a tool that stands to revolutionize playtesting--the process of playing a game to test it for bugs and flaws before it launches--for studios, with implications that extend far beyond the world of entertainment. In essence, Game Simulation equips game developers with the ability to create simulations with bots that playtest games on their behalf. Bots have been playing games effectively for decades, but Game Simulation harnesses them at a scale that is many orders of magnitude beyond what has been possible before.
Here's how you can still get a free Windows 10 upgrade You can still use Microsoft's free upgrade tools to install Windows 10 on an old PC running Windows 7 or Windows 8.1. No product key is required, and the digital license says you're activated and ready to go. Like previous years, 2018 featured a bevy of buzzword-laden technologies, but we at ZDNet are fatigued by the never-ending stream of acronyms. With that fatigue in mind, we put together a simple test for the year in technology. What technologies talked about today will actually matter in a decade?
BERLIN, Nov 21 (Reuters) - German data mining software firm Celonis said on Thursday that it had raised $290 mln in a Series C funding round, putting a $2.5 billion valuation on the company that has been compared with enterprise application giant SAP . The funding round was led by Arena Holdings and investors included Ryan Smith, the founder of customer experience specialist Qualtrics that was bought by SAP for $8 billion a year ago. Celonis, based in Munich and New York, runs a cloud-based service that uses artificial intelligence to mine data and optimise business processes, serving customers including Siemens, 3M, Airbus and Vodafone. "We are in a market that shows enormous momentum," co-CEO and co-founder Bastian Nominacher told Reuters, adding that Celonis would invest the funds raised in its global sales and customer service and in enhancing its cloud platform. The funding round brings total investments into Celonis to $370 million.
The year was 2008 and the Interop tech trade show in New York City was crammed full of booths. Sales reps offered trinkets as they hawked their next-gen software and hardware. I wondered through blinking displays and the noise of a thousand buzzwords. The brand name vendors – Microsoft, Intel, Oracle, IBM – had paid big bucks for booths the size of small houses. Staffers gave product lectures backed by full-size video screens. After touring these big outfits, I investigated the smaller booths hosted by mid-sized players. With minimal staff, they worked still harder to lure you to their pitch. In an era before "Booth Babes" were outlawed, some booths included twenty-something women in skimpy sequined outfits, handing out t-shirts or glow-in-the-dark key chains. Tiny booths staffed mostly by bare bones crews. There I saw a modest booth by an outfit called Amazon Web Services. A sole rep manned it, and he wasn't wearing a company shirt.
With less than a week to go, the excitement and anticipation are building up for industry's largest cloud computing conference - AWS re:Invent. As an analyst, I have been attempting to predict the announcements from re:Invent (2018, 2017) with decent accuracy. But with each passing year, it's becoming increasingly tough to predict the year-end news from Vegas. Amazon is venturing into new areas that are least expected by the analysts, customers, and its competitors. AWS Ground Station is an example of how creative the teams at Amazon can get in conceiving new products and services.
German data mining software firm Celonis said on Thursday that it had raised $290 mln in a Series C funding round, putting a $2.5 billion valuation on the company that has been compared with enterprise application giant SAP . The funding round was led by Arena Holdings and investors included Ryan Smith, the founder of customer experience specialist Qualtrics that was bought by SAP for $8 billion a year ago. Celonis, based in Munich and New York, runs a cloud-based service that uses artificial intelligence to mine data and optimize business processes, serving customers including Siemens, 3M, Airbus and Vodafone. "We are in a market that shows enormous momentum," co-CEO and co-founder Bastian Nominacher told Reuters, adding that Celonis would invest the funds raised in its global sales and customer service and in enhancing its cloud platform. The funding round brings total investments into Celonis to $370 million.
A new white paper is available showing the advantages of running virtualized Spark Deep Learning workloads on Kubernetes. Recent versions of Spark include support for Kubernetes. For Spark on Kubernetes, the Kubernetes scheduler provides the cluster manager capability provided by Yet Another Resource Negotiator (YARN) in typical Spark on Hadoop clusters. Upon receiving a spark-submit command to start an application, Kubernetes instantiates the requested number of Spark executor pods, each with one or more Spark executors. The benefits of running Spark on Kubernetes are many: ease of deployment, resource sharing, simplifying the coordination between developer and cluster administrator, and enhanced security.