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000-202 exam Dumps Source : Enterprise Storage Technical Support(R) Specialist V1

Test Code : 000-202
Test name : Enterprise Storage Technical Support(R) Specialist V1
Vendor name : IBM
: 59 existent Questions

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IBM IBM Enterprise Storage Technical

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IBM reinforces commercial enterprise multicloud growth with automation tools, ServiceNow expansion | killexams.com existent Questions and Pass4sure dumps

IBM is asking to accomplish it simpler for consumers to stir to multicloud environments by adding automation tools to its cloud capabilities, and the company is extending its relationship with cloud migration specialists ServiceNow.

The riding thought at the back of each strikes is to assist consumer simplify what may furthermore exist a daunting assignment – moving recent and legacy functions to multicloud environments exist they according to IBM's own cloud carrier or others akin to Amazon internet functions, Google Cloud, Microsoft Azure and crimson Hat.

As a backdrop to the recent capabilities, IBM closing month pointed out it would purchase open-source software pioneer crimson Hat in a $34 billion stock acquisition. For IBM the deal may imply many things. It makes it a bigger open source and commercial enterprise utility participant as an example, but in the main it receives great Blue into the lucrative hybrid-cloud party targeting its towering rivals Google, Amazon and Microsoft amongst others. Gartner says that market may exist value $240 billion with the aid of subsequent year.

a benchmark cloud manner requires re-hosting, re-platforming and re-architecting that can capture weeks to just a few months counting on the readiness and quantity of applications to migrate. In creating hybrid and multicloud environments, companies pan the challenge of needing to circulate and manage elevated volumes of facts, capabilities and workflows with out inflicting enterprise disruption, IBM says.

And that’s the plot the brand recent capabilities Come in. IBM has rolled out recent automation elements to its Cloud Migration manufacturing unit carrier that Big Blue says will assist purchasers reduce the time it takes to migrate and modernize applications for a multicloud or hybrid cloud deployment. IBM Cloud Migration manufacturing facility usually handles records validation including counsel on the source environment, migration-server setup, server inventory, technical specifications, functions, access credentials and creating a migration plan.

the recent toolset comprises algorithms derived “from years of knotty migration and modernizations that champion enhance planning accuracy and reduce hazards through uncovering dependencies and technical and system challenges a lot previous within the lifecycle,” IBM says. With these capabilities, IBM Cloud Migration manufacturing unit allows for smoother and sooner cloud adoption throughout dissimilar platforms, the traffic says.

Easing the stir to hybrid and multicloud environments is the understanding at the back of IBM extending its carrier family members with ServiceNow. both corporations teamed up in 2017 with a goal to fuse ServiceNow’s ’s cloud-based mostly carrier-automation platform and IBM cloud products and features.

today’s announcement mixes IBM’s recently announced Multicloud administration Platform with ServiceNow's IT provider administration and IT Operations administration capabilities.

announced in October, IBM’s Kubernetes-primarily based Multicloud manager runs on the business’s year-old IBM Cloud private platform but lets customers manage and integrate workloads on clouds from other providers corresponding to Amazon, Microsoft and red Hat. on the coronary heart of the Multicloud supervisor is a dashboard interface for managing lots of Kubernetes applications and Big volumes of records even with the plot within the organization they can exist found.

IBM says the extended ServiceNow partnership will aid shoppers with:

  • Agile software structure and comprehensive services for managing cloud lifecycle and traffic resources.
  • The potential to manage heterogeneous cloud environments quicker, greater can charge-readily and with improved consistency.
  • Visibility into multi-cloud operations to manage provider health and assist optimize service birth and charges.
  • join the community World communities on facebook and LinkedIn to palpate upon topics that are suitable of intellect.

    IBM (IBM) Down 10.three% considering final revenue document: Can It Rebound? | killexams.com existent Questions and Pass4sure dumps

    A month has gone by for the understanding that the remaining revenue document for IBM (IBM). Shares beget lost about 10.three% in that time frame, underperforming the S&P 500.

    Will the synchronous negative style continue leading as much as its next profits release, or is IBM due for a breakout? before they dive into how buyers and analysts beget reacted as of late, let's capture a short contemplate on the most synchronous profits report so as to accumulate a far better deal with on the distinguished catalysts.

    IBM’s Q2 results handicap from can imbue slicing, reduce partake count

    IBM said third-quarter 2018 non-GAAP revenue of $three.42 per share, which beat the Zacks Consensus evaluate via yoke of cents. income per partake (EPS) improved 4.9% from the year-in the past quarter.

    The 12 months-over-12 months expand in EPS can furthermore exist attributed to tough pre-tax margin operating leverage (28 cents contribution) and aggressive partake buybacks (19 cents contribution). This turned into partly offset by lower revenues (seven cents poor beget an upshot on) and better tax cost (17 cents negative impact).

    Revenues of $18.seventy six billion lagged the Zacks Consensus evaluate of $19.10 billion and declined 2.1% on a 12 months-over-12 months basis. At regular forex (cc), revenues remained flat.

    IBM stated that signings plunged 21% to $8 billion. capabilities backlog declined three% from the 12 months-in the past quarter to $113 billion.

    Geographic income details

    Revenues from Americas inched up 1%, driven through endured expand in Canada and Latin the usa and modest expand in the united states.

    Europe, core-East and Africa reduced 2% from the year-in the past quarter, pushed by using decline in Germany and France, partially offset through expand in Spain and the United Kingdom.

    Asia-Pacific revenues declined 1% on a 12 months-over-year groundwork with modest boom in Japan.

    Strategic Imperatives boom Continues

    Strategic Imperatives (cloud, analytics, mobility and safety) grew 7% at cc from the yr-ago quarter to $9.three billion. safety revenues surged 34%. On a trailing 12-month groundwork, Strategic Imperatives revenues had been $39.5 billion, up 13% (11% at cc).

    Cloud revenues surged 13% from the year-in the past quarter to $4.6 billion. The annual speed rate for cloud as-a-service revenues expanded 24% at cc on a 12 months-over-year basis to $eleven.4 billion.

    Cloud revenues of $19 billion on a trailing 12-month basis improved 20% (18% at cc) and now debts for twenty-four% of IBM’s total revenues.

    Cognitive Revenues Decline

    Cognitive solutions’ revenues-exterior lowered 5.7% 12 months over year (down 5% at cc) to $4.15 billion. Segmental revenues referring to Strategic Imperatives and Cloud declined 4% and a yoke of%, respectively. Cloud as-a-carrier earnings annual speed rate changed into $2 billion.

    solutions utility contains choices in strategic verticals infatuation fitness, domain-specific capabilities infatuation analytics and security, and IBM’s rising applied sciences of AI and blockchain. The facet additionally contains offerings that address horizontal domains infatuation collaboration, commerce and talent. options software revenues lowered 3% yr over yr within the quarter.

    IBM pointed out that in commerce region the infusion of AI into choices infatuation consumer journey analytics helped SaaS signings to grow double digit in the quarter. The recent launch of Notes Domino version 10, which is optimized for cell, and supports JavaScript and node.js will raise boom collaboration in 2019.

    Transaction Processing software contains application that runs mission-important workloads, leveraging IBM’s hardware systems. Revenues fell eight% on a year-over-year groundwork.

    IBM witnessed expand in industry verticals infatuation health, key areas of analytics and protection within the quarter. Watson health witnessed vast-primarily based boom in Payer, company, Imaging and life Sciences domains.

    right through the quarter, the Sugar.IQ software, developed by route of Medtronic in partnership with IBM, hit the market. The utility is designed to simplify and expand daily diabetes administration.

    IBM brought up that analytics carried out smartly within the quarter, pushed by means of information science choices and IBM Cloud inner most for records offering.

    all through the quarter, the traffic announced warp detection functions and launched recent Watson services on the IBM Cloud deepest platform.

    safety expand turned into pushed by route of offerings in orchestration, information protection and endpoint management.

    In blockchain, IBM meals beget faith network for meals safeguard went are vital in the quarter. Reatiler Carrefour joined IBM’s blockchain network. The enterprise additionally collectively introduced TradeLens with Maersk that addresses inefficiencies within the international supply chain. IBM currently supports 75 energetic blockchain networks.

    international enterprise functions Revenues increase

    Revenues from international traffic features-external section had been $4.13 billion, up 0.9% from the year-in the past quarter (up 3% at cc). Segmental revenues pertaining to Strategic Imperatives grew 9%. Cloud keep surged 18%. Cloud as-a-provider income annual speed cost become $1.9 billion.

    application administration revenues declined 1% from the 12 months-ago quarter. besides the fact that children, international manner capabilities revenues climbed 2%. in addition, Consulting revenues accelerated 7% yr over yr, driven through tough performance from IBM’s digital enterprise.

    technology functions & Cloud structures: Revenues Dip

    Revenues from expertise functions & Cloud systems-exterior diminished 2% from the year-ago quarter (flat at cc) to $eight.29 billion. Segmental revenues pertaining to Strategic Imperatives superior 16%, driven by using hybrid cloud capabilities. Cloud surged 22% from the 12 months-in the past quarter. Cloud as-a-service earnings annual speed fee become $7.5 billion.

    Integration application increased 1% from the yr-in the past quarter. raw the route through the quarter, 95 companies around the world chosen IBM Cloud inner most offering. Infrastructure capabilities revenues furthermore multiplied 1% on a year-over-yr groundwork.

    besides the fact that children, Technical palliate services revenues decreased 3% from the 12 months-in the past quarter.

    vigor & z14 power methods Revenues

    programs revenues multiplied 0.9% on a yr-over-yr groundwork (up 2% at cc) to $1.74 billion. Segmental revenues touching on Strategic Imperatives surged 5%, whereas Cloud revenues declined 8%.

    IBM Z revenues accelerated 6% 12 months over 12 months on more than 20% MIPS increase, pushed by using vast-based adoption of the z14 mainframe.

    energy revenues elevated 17% from the 12 months-in the past quarter. raw over the quarter, IBM launched its next technology POWER9 processors for midrange and excessive-end systems that are designed for dealing with superior analytics, cloud environments and data-intensive workloads in AI, HANA, and UNIX markets.

    IBM additionally introduced recent choices optimizing both hardware and utility for AI. management believes that products infatuation PowerAI imaginative and prescient and PowerAI enterprise will champion power recent customer adoption.

    besides the fact that children, storage hardware revenues declined 6% due to vulnerable performance within the midrange and extravagant conclusion, partly offset by means of tough boom in raw flash Arrays. IBM mentioned that pricing constrain in the immensely aggressive storage market is hurting revenues. The company introduced its recent FlashSystems with subsequent generation NVMe expertise privilege through the quarter.

    operating techniques software revenues declined 4%, while techniques Hardware superior 4% from the 12 months-in the past quarter.

    at last, world Financing (includes financing and used machine income) revenues diminished 9.1% at cc to $388 million.

    operating particulars

    Non-GAAP raw margin remained unchanged from the year-in the past quarter at 47.4%. This was IBM’s optimal raw margin efficiency in years and turned into basically pushed by using 160 basis points (bps) expansion in capabilities margin. however, detrimental combine in z14 mainframe and utility totally offset this expansion.

    working charge declined four% yr over year, because of awareness of acquisition synergies and enhancing operational efficiencies. IBM continues to invest in quickly growing to exist fields infatuation hybrid cloud, synthetic intelligence (AI), security and blockchain.

    Pre-tax margin from carrying on with operations increased 50 bps on a yr-over-year foundation to 19.2%.

    Cognitive solutions and global traffic features section pre-tax margins increased 190 bps and 320 bps, respectively, on a yr-over-year groundwork. however, expertise capabilities & Cloud platforms facet pre-tax margin gotten smaller one hundred bps.

    techniques pre-tax revenue was $209 million down 38% yr over year. global Financing facet pre-tax salary jumped 26.7% to $308 million.

    balance Sheet & cash circulation details

    IBM ended third-quarter 2018 with $14.70 billion in complete money and marketable securities in comparison with $11.93 billion at the conclusion of second-quarter 2018. complete debt (including international financing) became $forty six.9 billion, up $1.4 million from the outdated quarter.

    IBM stated money circulation from operations (except for global Financing receivables) of $3.1 billion and generated free money circulation of $2.2 billion in the quarter.

    in the said quarter, the company returned $2.1 billion to shareholders via dividends and partake repurchases. on the cease of the quarter, the traffic had $1.four billion remaining beneath present buyback authorization.

    suggestions

    IBM reiterated EPS forecast for 2018. Non-GAAP EPS is anticipated to exist at the least $13.eighty.

    IBM still anticipates 2018 free cash stream of $12 billion.

    Story Continues

    How beget Estimates Been moving since Then?

    in the past month, buyers beget witnessed a downward trend in cleanly estimates.

    VGM ratings

    at this time, IBM has a typical growth score of C, although it's lagging a bit on the Momentum ranking entrance with a D. however, the inventory changed into allocated a grade of A on the charge facet, placing it within the exact quintile for this funding method.

    basic, the inventory has an aggregate VGM score of B. in case you aren't focused on one method, this score is the one you should exist drawn to.

    Outlook

    Estimates were largely trending downward for the inventory, and the magnitude of those revisions shows a downward shift. in particular, IBM has a Zacks Rank #3 (cling). They are expecting an in-line return from the inventory in the following few months.

    desire the newest recommendations from Zacks investment research? these days, that you would exist able to download 7 most desirable shares for the next 30 Days. click to accumulate this free report foreign traffic Machines traffic enterprise (IBM) : Free inventory analysis report To read this text on Zacks.com click on privilege here. Zacks investment research


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    000-202 exam Dumps Source : Enterprise Storage Technical Support(R) Specialist V1

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    Xenomorph Extends EDM NoSQL Integration with MarkLogic® Database | killexams.com existent questions and Pass4sure dumps

    NEW YORK & LONDON & SINGAPORE--(BUSINESS WIRE)--Xenomorph, the data and analytics people, today announced that its TimeScape EDM+, Xenomorph’s enterprise data management (EDM) solution for financial markets, has been integrated with the MarkLogic® database, a leading operational and transactional Enterprise NoSQL database.

    TimeScape EDM+ offers ready-to-run data integration, cleansing, validation, audit, analysis and distribution for reference, market and derived data. Clients can leverage raw of this functionality integrated with the ACID compliant, scale-out, bitemporial capabilities of the MarkLogic database. Both structured and unstructured data stored in the MarkLogic database can exist integrated with data and analytics in TimeScape EDM+, enabling traffic users to access, validate and analyse these combined datasets without any specialist technical knowledge. A user can access data through one family of consistent TimeScape EDM+ interfaces whether their preference is Excel, a programming API infatuation Python, BI platforms infatuation Tableau and Power BI, or mathematical environments infatuation Matlab and R.

    “The integration of TimeScape EDM+ with the MarkLogic database continues their efforts to explore the benefits of NoSQL and scale-out SQL technologies applied to EDM in financial markets,” says Chris Budgen, Chief Technical Architect, Xenomorph. “The combination of document and semantic storage within the MarkLogic database is an spellbinding meet with the TimeScape database model, enabling easy champion of recent asset classes through schema flexibility combined with easy definition of relationships between different financial objects, such as those from a curve to its underlying data points.”

    “The data management and analytical needs of financial markets customers continues to exist challenging due to the competitive and regulatory environment. This partnership will palliate the financial service providers to address those requirements,” said David Ponzini, Senior Vice President, Corporate development and Marketing, MarkLogic Corporation. “The combination of their enterprise-strength, resilient database platform and TimeScape EDM+ offers a robust, agile, solution that rationalizes data logistics and improves time-to-market deployment.”

    “The integration of TimeScape EDM+ with the MarkLogic database extends the ways in which their clients can manage, validate and analyse data,” adds Brian Sentance, CEO, Xenomorph. “Clients pan many data management challenges currently, not least of which are the changes introduced by recent regulations such as FRTB and IFRS 9. Delivering data management solutions that beget the flexibility to instantly integrate recent asset classes, recent datasets and recent types of data into these recent requirements is a key to traffic success in coming years. TimeScape EDM+ with the MarkLogic database offers one route in which these issues can exist addressed through the combination of technical scale and traffic flexibility.”

    About Xenomorph

    Xenomorph believes that distinguished decisions are made when everyone can easily access and analyze the same elevated quality data. Based on this belief, they develop software solutions for financial markets that enable both technologists and traffic users alike to manage, cleanse and analyze more data, more quickly. These solutions embrace an Enterprise Data Management system, TimeScape EDM+, a cloud-based data publishing service, TimeScape MarketPlace and their underlying data and analytics technology platform, TimeScape. Trading, research, risk, product control, IT and back-office staff exercise these solutions at investment banks, brokerages, insurers, hedge funds and asset management companies worldwide. Established in 1995, Xenomorph has offices in London, recent York and Singapore.

    For more information on Xenomorph, their clients, services and solutions, delight visit www.xenomorph.com.

    Xenomorph® and TimeScape™ are registered trademarks of Xenomorph Software Limited. Other product and company names herein may exist trademarks of their respective owners.

    About MarkLogic

    For over a decade, organizations around the world beget Come to reckon on MarkLogic to power their innovative information applications. As the world’s experts at integrating data from silos, MarkLogic’s operational and transactional Enterprise NoSQL database platform empowers their customers to build next generation applications on a unified, 360-degree view of their data. Headquartered in Silicon Valley, MarkLogic has offices throughout the U.S., Europe, Asia, and Australia.

    For more information, delight visit www.marklogic.com.

    MarkLogic is a registered trademark of MarkLogic Corporation in the United States and/or other countries. raw other trademarks mentioned are the property of their respective owners.


    Cisco Announces Open Network Environment to Unleash Application-Driven Network Programmability | killexams.com existent questions and Pass4sure dumps

    Cisco Live! San Diego – June 13, 2012 –  Cisco today introduced a versatile and broad approach to network programmability— Cisco ® Open Network Environment (Cisco ONE) —  aimed at helping  customers  drive the next wave of traffic innovation through trends such as cloud, mobility, companionable networking, and video. Cisco ONE enables flexible, application-driven customization of network infrastructures to palliate realize traffic objectives such as:  increased service velocity, resource optimization, and faster monetization of recent services.

    The Cisco Open Network Environment is delivered through a moneyed set of platform APIs, agents and controllers, and overlay network technologies. Cisco ONE complements current approaches to software-defined networking while encompassing the entire solution stack from transport to management and orchestration.  With Cisco's Open Network Environment customers can harness the intelligent network through programmability and abstraction across multiple layers, offering a election of protocols, industry standards, and usage-based deployment models. 

    As Part of the Open Network Environment, Cisco announced One Platform Kit (onePK) which provides application programming interfaces (APIs) for developers across Cisco operating systems: Cisco IOS ®, IOS-XR, and NX-OS. Cisco furthermore announced proof-of-concept controller software and proof-of-concept OpenFlow agent for Software Defined Networking (SDN) research.  Cisco is furthermore enabling scalable virtual overlay networks for multi-tenant cloud deployments with the Cisco Nexus® 1000V virtual switch. recent innovations include: OpenStack support, programmability, multi-hypervisor capability, and VXLAN gateway functionality.

    Cisco is collaborating on emerging network technologies with industry leaders, academic organizations, and standards bodies to meet their heterogeneous requirements for network programmability. Cisco's Open Network Environment supports a wide variety of deployment models including: 

  • Universities and Research Organizations: Network partitioning or "campus network slicing" using proof-of-concept  controller software and OpenFlow agents for SDN research
  • Hyperscale Data centers: Network tide management with programmatic access via APIs
  • Cloud Providers: Automated provisioning and programmable overlay network for scalable multi-tenancy
  • Service Providers: Programmatic access, policy and analytics to optimize and monetize service delivery
  • Enterprises: Private cloud automation for virtual workloads , including VDI
  • NEWS HIGHLIGHTS

    Cisco Open Network Environment is adopting a multi-pronged product strategy to deliver network programmability.

    One Platform Kit (onePK) Software development kit for customers and partners

  • onePK delivers a moneyed set of APIs for deep programmatic access across Cisco operating systems and hardware platforms. 
  • Cisco will provide phased champion for onePK on IOS, IOS-XR, and NX-OS operating systems and hardware platforms including ASR, ISR G2, CRS, catalyst ® and Nexus® switches.  Cisco onePK champion will exist provided initially for the ASR1000 and ISRG2.
  • onePK allows taut integration with software applications for greater visibility, orchestration and control of network infrastructure for rapid service creation and delivery, and automate multiple network tasks and processes.     
  • With onePK, developers can select the language they are confidential with, exercise existing tools, and easily integrate into existing development environments.    
  • On June 12 Cisco announced its Cloud Connected Solution Partners and customers can furthermore develop their own Cloud Connectors using onePK APIs to enhance and differentiate Cloud Services.  Cloud Connector is a software component residing on ISR G2 and ASR1000 routing platforms to ameliorate availability, performance, and security for cloud services.  
  • Cisco Controller Software and OpenFlow Agents for Software Defined Networking (SDN)

  • Cisco will provide proof-of-concept controller software for SDN and is collaborating with academic and research institutions in these efforts. For example, universities will exist able to partition or "slice" their campus network to enable academic research on SDN with consistent policy management.
  • Cisco will provide proof-of-concept OpenFlow v1.0 agents on the Cisco catalyst 3750-X and 3560-X series Switches.
  • Cisco is furthermore leading several workgroups at the Open Network Foundation.
  • Virtual Overlay Networks extend champion for OpenStack, multiple hypervisors,  and VXLAN gateway functionality for consistency across physical and virtual networks

  • Cisco will expand virtual overlay network solutions for scalable, multi-tenant cloud infrastructures and Infrastructure-as-a-Service (IAAS) through enhanced capabilities of its Nexus 1000V portfolio with champion for open-source hypervisors.  This adds to existing champion for VMware and Microsoft hypervisors.
  • Cisco will furthermore provide the Virtual Extensible Local region Network (VXLAN)  gateway to bridge traditional VLAN-based physical network environments with VXLAN-based virtual overlay networks.
  • Cisco will extend vPath technology implemented on Nexus 1000V to champion virtual services on VXLAN-based overlay networks, including VSG and ASA1000V.  Cisco vPath will steer or "chain" traffic across VSG and ASA1000V through pre-defined policies to simplify operational workflows on a per-tenant basis.
  • Cisco will tender standards-based OpenStack Quantum Plug-in and comfort APIs for the Cisco Nexus 1000V virtual switch to rapidly orchestrate multi-tenant cloud infrastructures.
  • Availability

    Beta trials and phased general availability are scheduled to originate the last quarter of 2012.

    SUPPORTING QUOTES

    Cisco:  Padmasree Warrior, CTO, SVP Engineering, GM Enterprise Business   "The Cisco Open Network Environment is key to their vision of an intelligent network that is more open, programmable, and application-aware—  a vision in which the network is transformed into a more effectual traffic enabler," said Padmasree Warrior, Cisco chief technical officer, senior vice president of Engineering, and general manager of Enterprise Business. "With Cisco's Open Network Environment, customers accumulate a comprehensive and robust set of capabilities to address emerging enterprise and service provider requirements to harness network intelligence and provide value for recent traffic platforms in an evolutionary manner."  

    Cisco:  David Yen, SVP and GM of the Data hub Technology Group "Today's announcement is a milestone event that signals Cisco's leadership, contribution and dedication to open networking," said David Yen, senior vice president and general manager of the Data hub Technology Group at Cisco. "Using the Cisco ONE Platform Kit, developers will beget access to over 20 years of intelligent networking software produced by Cisco's top R&D talent.  Together, we'll usher in a recent era of programmability through transparent and granular interaction between applications and network infrastructure."

    CTERA: Liran Eshel, chief executive officer:  "By leveraging Cisco ISR G2 as a platform, CTERA and Cisco allow customers to bridge the gap between local and cloud storage by combining LAN speed user suffer with the resilience of the cloud," said Liran Eshel, chief executive officer at CTERA. "Utilizing the ISR's network capabilities, the consolidated solution can effectively manage cloud storage traffic in an optimized way. The integration, which was rapidly implemented as a connector using Cisco's Open Network Environment and the onePK API, allowed us to deliver the world's first cloud-storage-enabled router with intelligent QoS and superior user experience."

    MLB.com:  Kushal Patel, VP of Networking  "Baseball has dedicated and passionate fans who beget elevated expectations for the quality and value of their interactive experiences, which demands a amenable network," said Kushal Patel, vice president of networking at MLB.com.  "We infatuation the strategic direction Cisco is taking with its Open Network Environment, which provides a platform to more closely integrate customer-built software applications with the network."

    Indiana University:  Brad Wheeler, vice president of information technology and CIO "Indiana University is delighted to beget Cisco as the newest member of InCNTRE's SDN Interoperability Lab," said Brad Wheeler, Indiana University vice president of information technology and CIO.  "This collaboration, along with Cisco's vision for dynamic and multi-dimensional networks – the Open Network Environment-  will palliate drive innovation to advance next generation networks."

    University of Wisconsin Madison:  Bruce Maas, Vice Provost for Information Technology and CIO  "Our IT network engineers and computer science department researchers beget been collaborating with Cisco to develop and advance SDN solutions that will palliate stir SDN from the R&D lab to mainstream traffic and academic production environments," said  Bruce Maas, vice provost for information technology and CIO at University of Wisconsin Madison. They believe that programmable networks—providing program interfaces to devices and software that capture handicap of network intelligence-will enable recent research innovations that will advance science and boost economic development."  

    HCL Technologies LTD:  Prabhuraman Sayanam, Vice President   "As a leading global IT and software development company, HCL Technologies is always interested in taking handicap of network innovations," said Prabhuraman Sayanam, vice president at HCL Technologies LTD. "As one of the first developer partners for Cisco's onePK, they beget already begun working with the application programming interface.  We're looking forward to using Cisco's Open Network Environment and onePK to create innovative recent software applications to serve their joint customers' needs."

    SUPPORTING RESOURCES

    About Cisco

    Cisco (NASDAQ: CSCO) is the worldwide leader in networking that transforms how people connect, communicate and collaborate.  Information about Cisco can exist organize at http://www.cisco.com. For ongoing news, delight fade to http://newsroom.cisco.com.

    # # #

     Cisco and the Cisco logo are trademarks or registered trademarks of Cisco and/or its affiliates in the U.S. and other countries. A listing of Cisco's trademarks can exist organize at www.cisco.com/go/trademarks. Third-party trademarks mentioned are the property of their respective owners. The exercise of the word partner does not imply a partnership relationship between Cisco and any other company.

    # # #


    Fact Sheet: Unleashing the Power of artificial Intelligence in Healthcare with Intel Technologies | killexams.com existent questions and Pass4sure dumps

    Artificial Intelligence: Machine Learning, deep Learning and More

    Artificial intelligence (AI) is now raw around us, as machines increasingly gain the faculty to sense, learn, reason, act and reconcile in the existent world. Much of the AI spotlight is focused on deep learning, a offshoot of machine learning that uses neural networks to comprehend knotty and unstructured data (training) and exercise that understanding to classify, recognize and process recent inputs (inference). deep learning is delivering breakthroughs in areas infatuation image recognition, speech recognition, natural language processing and other knotty tasks.

    Processing deep Learning Workloads in Healthcare

    Deep learning workloads require a tremendous amount of computational power to speed knotty mathematical algorithms and process huge amounts of data. While GPUs beget been used for some deep learning training applications, they don’t necessarily beget a performance edge for deep learning inference. In healthcare in particular, there are many examples of applications that speed better on CPUs than GPUs.

    That’s why for healthcare organizations running AI workloads, Intel® Xeon® Scalable processors provide an ideal computational foundation. Intel Xeon Scalable processors are optimized for AI, scale up quickly and seamlessly for 2.1x faster deep learning performance over previous generations1, and tender server-class reliability and workload flexibility1.

    Versatility

    Many organizations that are implementing AI don’t necessity to train models 24/7. Those that carry out necessity to train models around the clock require a dedicated accelerator, but the majority of organizations simply necessity to train and then deploy a model. If an organization invests thousands of dollars in structure a dedicated acceleration stream, it can’t carry out anything else with that infrastructure beyond training – it will just sit idle.

    Intel Xeon Scalable processors are more agile. They are designed to flex with traffic needs to champion the workload needs of the moment, allowing organizations to leverage their data hub infrastructure for AI applications and a wide scope of other workloads.

    Increased Utilization

    Most organizations beget at least 35 percent free utilization of processing capacity, if not more. This means they beget could exist obtaining better ROI from their infrastructure investments. With Intel Xeon Scalable processors, they can leverage this unused capacity to implement AI while still meeting the needs of other applications.

    Technical Advantages

    GPUs are often leveraged as an accelerator, but they beget limitations when it comes to structure models to tackle healthcare issues. These limitations originate with memory. GPUs are limited to only 12 to 16 gigabytes of memory on the chip itself, and that restricts the size and capabilities of the model that can exist built. This memory limitation is a particular problem in healthcare AI work, which often involves structure massive models – for example, to champion a giant CT scan.

    For today’s AI networks deployed on a benchmark GPU, the image size for a scan that can exist developed is usually 256 x 256 pixels. At the most, that image could exist 1000 x 1000 pixels, but that’s pushing it. If clinicians beget a 4K image where they actually necessity raw of those pixels to accomplish an analysis — for example, to detect a tumor in a tiny portion of the image — then they beget a problem. When they scale that 4K image down to 256 x 256 pixels, they lose raw of the fine temporal resolution.

    The same model can exist built with Intel Xeon Scalable processors without limiting the size and resolution because the Intel Xeon Scalable family offers a terabit of on-load memory. Intel Xeon processors may process the image more slowly than a GPU, but when the resolution is needed for medical imaging, Intel Xeon processors deliver.

    A Processor Designed for deep Learning

    Moving forward, Intel has furthermore developed the Intel® Nervana™ Neural Network Processor (NNP), the world’s first processor specifically designed from the ground up for deep learning. The Intel Nervana NNP promises to further enhance medical imaging and other healthcare applications.

    Using the Intel Nervana platform, healthcare organizations can develop entirely recent classes of AI applications that maximize the amount of data processed and lead to greater insights. For example, AI will allow for earlier diagnosis and greater accuracy, helping accomplish the impossible workable by advancing research on cancer, Parkinson’s disease and other brain disorders2.

    The Intel handicap for AI

    Intel has 50 years of suffer in helping its customers accomplish their data valuable. Today, Intel is using that suffer to palliate healthcare organizations address every aspect of their workflows through the successful implementation of AI.

    From hardware to software and data science, Intel brings its plenary suite of products and expertise to AI. Intel has the technical expertise not only to palliate organizations build the privilege infrastructure – from data to storage and network – but furthermore has the data scientists to understand and model data and the application developers to palliate accomplish the data useful. In short, Intel’s expertise to implement AI goes well beyond the CPU.

    Driving the Age of AI

    At Intel, they recognize the age of AI is upon us, and they know their technologies will palliate drive the future of AI. They are furthermore motivated by a wish to exercise their technology to palliate advance society and tackle the world’s Big challenges, so we’re particularly excited about their travail in healthcare. They want to palliate channel AI for societal pleasant – they summon it AI with a purpose.

    With these thoughts in mind, we’re pleased to exist engaged with healthcare providers and researchers. They know that their collective travail is helping stir the needle – with even more swear and unlimited possibilities for the future.

    Benchmark results were obtained prior to implementation of recent software patches and firmware updates intended to address exploits referred to as “Spectre” and “Meltdown”.  Implementation of these updates may accomplish these results inapplicable to your device or system.

    Software and workloads used in performance tests may beget been optimized for performance only on Intel microprocessors.

    Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may occasions the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit www.intel.com/benchmarks.

    1INFERENCE using FP32 Batch Size Caffe GoogleNet v1 256  AlexNet 256.

    Performance estimates were obtained prior to implementation of recent software patches and firmware updates intended to address exploits referred to as “Spectre” and “Meltdown.”  Implementation of these updates may accomplish these results inapplicable to your device or system. Software and workloads used in performance tests may beget been optimized for performance only on Intel microprocessors.  Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions.  Any change to any of those factors may occasions the results to vary.  You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit http://www.intel.com/performance  Source: Intel measured as of June 2017 Optimization Notice: Intel’s compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations embrace SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for exercise with Intel microprocessors. inevitable optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. delight mention to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

    Configurations for Inference throughput

    Processor :2 socket Intel(R) Xeon(R) Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , Turbo ON Total memory 376.46GB (12slots / 32 GB / 2666 MHz).CentOS Linux-7.3.1611-Core , SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD 1.5TB,sdc RS3WC080 HDD 5.5TB , deep Learning Framework caffe version: f6d01efbe93f70726ea3796a4b89c612365a6341 Topology :googlenet_v1 BIOS:SE5C620.86B.00.01.0004.071220170215 MKLDNN: version: ae00102be506ed0fe2099c6557df2aa88ad57ec1 NoDataLayer. Measured: 1190 imgs/sec vs Platform: 2S Intel® Xeon® CPU E5-2699 v3 @ 2.30GHz (18 cores), HT enabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 256GB DDR4-2133 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.el7.x86_64. OS drive: Seagate* Enterprise ST2000NX0253 2 TB 2.5″ Internal hard Drive. Performance measured with: Environment variables: KMP_AFFINITY=’granularity=fine, compact,1,0‘, OMP_NUM_THREADS=36, CPU Freq set with cpupower frequency-set -d 2.3G -u 2.3G -g performance. deep Learning Frameworks: Intel Caffe: (http://github.com/intel/caffe/), revision b0ef3236528a2c7d2988f249d347d5fdae831236. Inference measured with “caffe time –forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (GoogLeNet, AlexNet, and ResNet-50), https://github.com/intel/caffe/tree/master/models/default_vgg_19 (VGG-19), and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to exercise newer Caffe prototxt format but are functionally equivalent). GCC 4.8.5, MKLML version 2017.0.2.20170110. BVLC-Caffe: https://github.com/BVLC/caffe, Inference & Training measured with “caffe time” command.  For “ConvNet” topologies, dummy dataset was used. For other topologies, data was st ored on local storage and cached in memory before training  BVLC Caffe

    Configuration for training throughput:

    Processor :2 socket Intel(R) Xeon(R) Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , Turbo ON Total memory 376.28GB (12slots / 32 GB / 2666 MHz).CentOS Linux-7.3.1611-Core , SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD 1.5TB,sdc RS3WC080 HDD 5.5TB , deep Learning Framework caffe version: f6d01efbe93f70726ea3796a4b89c612365a6341 Topology :alexnet BIOS:SE5C620.86B.00.01.0009.101920170742 MKLDNN: version: ae00102be506ed0fe2099c6557df2aa88ad57ec1 NoDataLayer. Measured: 1023 imgs/sec vs Platform: 2S Intel® Xeon® CPU E5-2699 v3 @ 2.30GHz (18 cores), HT enabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 256GB DDR4-2133 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.el7.x86_64. OS drive: Seagate* Enterprise ST2000NX0253 2 TB 2.5″ Internal hard Drive. Performance measured with: Environment variables: KMP_AFFINITY=’granularity=fine, compact,1,0‘, OMP_NUM_THREADS=36, CPU Freq set with cpupower frequency-set -d 2.3G -u 2.3G -g performance. deep Learning Frameworks: Intel Caffe: (http://github.com/intel/caffe/), revision b0ef3236528a2c7d2988f249d347d5fdae831236. Inference measured with “caffe time –forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (GoogLeNet, AlexNet, and ResNet-50), https://github.com/intel/caffe/tree/master/models/default_vgg_19 (VGG-19), and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to exercise newer Caffe prototxt format but are functionally equivalent). GCC 4.8.5, MKLML version 2017.0.2.20170110. BVLC-Caffe: https://github.com/BVLC/caffe, Inference & Training measured with “caffe time” command.  For “ConvNet” topologies, dummy dataset was used. For other topologies, data was st ored on local storage and cached in memory before training  BVLC Caffe (http://github.com/BVLC/caffe), revision 91b09280f5233cafc62954c98ce8bc4c204e7475 (commit date 5/14/2017). BLAS: atlas ver. 3.10.1.

    2Intel editorial by Brian Krzanich, chief executive officer of Intel Corporation, “Announcing Industry’s First Neural Network Processor,” October 17, 2017.

    Intel, the Intel logo, Xeon, and Nervana are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

    Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. Check with your system manufacturer or retailer or learn more at www.intel.com.



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