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70-774 exam Dumps Source : Perform Cloud Data Science with Azure Machine Learning?

Test Code : 70-774
Test denomination : Perform Cloud Data Science with Azure Machine Learning?
Vendor denomination : Microsoft
: 37 existent Questions

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Microsoft: ANSYS CFD and Microsoft Azure perform finest HPC scalability within the cloud | killexams.com existent Questions and Pass4sure dumps

Over previous a few months we've made announcements with part partners within the simulation ecosystem in regards to the daily performance of various solver applied sciences on Microsoft Azure massive Compute. today they are delighted to proclaim the addition of ANSYS computational fluid dynamics (CFD) solvers to this ecosystem.

impartial software dealer (ISV) applications fancy ANSYS CFD are on the heart of CFD simulations. Microsoft Azure “huge Compute” can deliver heaps of cores on require to ANSYS consumers to hasten their complicated CFD simulation initiatives. These simulations should subsist backed by using HPC infrastructure in Azure enabled via RDMA and InfiniBand technology obtainable for each Linux and home windows.

ANSYS CFD application enables engineers to operate multi-physics evaluation, including fluid forces, thermal consequences, structural integrity and electromagnetic radiation to examine and totally optimize their items.

“ANSYS and Microsoft Azure had been working closely on a Proof of concept (POC) with a big customer to hasten ANSYS CFD workload on Azure,” pointed out Ray Milhem, vice chairman of industry solutions at ANSYS. “The POC proved very a hit and the data showed remarkable scalability working ANSYS CFD as much as 1024 cores.”

study the entire article here, ANSYS CFD and Microsoft Azure perform most efficient HPC scalability within the cloud

by passage of the pleasant folks at Microsoft.


Microsoft says future facts centres could subsist underwater | killexams.com existent Questions and Pass4sure dumps

speakme at the Microsoft’s Future Decoded conference in London, CEO Satya Nadella talked about that half of the enterprise’s plans for future statistics centers includes underwater server farms, Ars Technica experiences.

Microsoft has been experimenting with underwater servers for some time, and in 2016 venture Natick set a server pod underwater off the coast of California.

The pod became designed as a sealed unit and uses water cooling to dump fritter warmth into the ocean round it. It was designed to subsist deployed for five years earlier than being introduced back as much as the surface to subsist replaced.

Undersea deployments are “the manner [Microsoft] will feel about facts headquarters areas and growth”, Nadella observed.

Deploying servers within the ocean potential they can likewise subsist near inhabitants centers, ensuring lower latencies, which Nadella mentioned as a specific potential. He added that for real-time services, including Microsoft’s approaching Xcloud game streaming provider, low latencies are specifically crucial.

yet another expertise is the hurry at which servers may likewise subsist deployed the usage of this underwater method.

He noted that with out the need to build an specific statistics centre, the pods can subsist deployed very without delay.

Now read: Microsoft upgrades its streaming platform to remove on Twitch

Alteryx Now permits records Analysts To role In-Database blending In Microsoft Azure SQL facts Warehouse | killexams.com existent Questions and Pass4sure dumps

IRVINE, Calif.--(company WIRE)--Alteryx, Inc., the chief in self-provider information analytics, nowadays announced further product integration with Microsoft Corp., to empower information analysts with a less complicated option to operate In-database mixing in one locality with Microsoft Azure SQL statistics Warehouse. This aggregate provides analysts with a higher solution to remove skills of self-carrier records analytics that leads to deeper insights in hours, now not the weeks ordinary of usual methods. With in-database mixing, analysts gain a competitive abilities with the cloud-computing vigour offered on Microsoft Azure to scale efficiency of their facts blending, enabling them to operate greater advanced operations and in the reduction of time to perception before pushing without detain to Microsoft power BI for wider consumption across the business.

“Alteryx now makes it even less demanding for data analysts to operate broad data processing within the cloud with in-database blending via Microsoft Azure SQL records Warehouse,” noted Paul Ross, vice president of overseas and Channel advertising at Alteryx, Inc. “Alteryx Self-provider statistics analytics with out coding permits analysts to create information models visually and then hold Alteryx execute the tactics in the Microsoft Azure cloud atmosphere earlier than pushing the statistics to Microsoft power BI to imagine every exiguous thing in one vicinity.”

“we've seen a growing to subsist number of analysts the employ of in-database blending to hurry up their time to perception,” talked about Tiffany Wissner, Senior Director of statistics Platform advertising at Microsoft. “Now, greater users can remove skills of this time to perception by using Alteryx to operate in-database mixing in the Microsoft Azure SQL data Warehouse service. Combining the powers of Alteryx and Microsoft Azure SQL records Warehouse empowers analysts with even more scalability and efficiency within the cloud.”

This latest integration between Alteryx and Microsoft provides facts analysts the means to:

  • connect with Azure SQL records Warehouse as an information supply – The faculty to at once and simply blend data from the database with different facts sources while the employ of the processing vigour of the facts warehouse creates a richer facts set for deeper insights and speeds the birth of analytic outcomes.
  • operate In-Database mixing Operations – statistics analysts are able to enlarge their statistics mixing efficiency via affecting the processing to the database rather than from a computer and carry quicker records mixing it is capable of scale as your records changes. This integration additionally allows analysts to operate tremendous scale mixing and advanced analytics with out the need to know or employ SQL coding.
  • Output to Microsoft energy BI – ultimately during the existing Alteryx and Microsoft relationship, analysts can accomplish the most of the Microsoft stack and thrust their blended and analyzed dataset at once to vitality BI to create prosperous visualizations, dashboards, and experiences to participate deeper insights inside their company.
  • more information on Alteryx and Microsoft can likewise subsist organize at http://www.alteryx.com/microsoft. Alteryx offers a Starter package for Microsoft with analytics examples to advocate consumers gain began. This apparatus may likewise subsist downloaded at http://www.alteryx.com/microsoftkit.

    additionally, subsist piece of Alteryx and Microsoft for a Webinar on can likewise tenth featuring Azure SQL records Warehouse and Alteryx integration to subsist taught extra about harnessing the vigour of in-database mixing. Registration is purchasable here.

    About Alteryx, Inc.

    Alteryx is the chief in self-provider records analytics. Alteryx Analytics gives analysts with the unique potential to conveniently prep, blend and resolve indecent of their records using a repeatable workflow, then install and participate analytics at scale for deeper insights in hours, no longer weeks. Analysts adore the Alteryx Analytics platform as a result of they can connect to and cleanse information from facts warehouses, cloud applications, spreadsheets and other sources, quite simply subsist a piece of this statistics together, then perform analytics – predictive, statistical, and spatial – the usage of the identical intuitive user interface, devoid of writing any code. lots of companies and data analysts international count on Alteryx each day. debate with www.alteryx.com or denomination 1-888-836-4274.

    Alteryx is a registered trademark of Alteryx, Inc. indecent different product and brand names can subsist emblems or registered logos of their respective owners.




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    70-774 exam Dumps Source : Perform Cloud Data Science with Azure Machine Learning?

    Test Code : 70-774
    Test denomination : Perform Cloud Data Science with Azure Machine Learning?
    Vendor denomination : Microsoft
    : 37 existent Questions

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    Perform Cloud Data Science with Azure Machine Learning?

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    Microsoft revamps machine learning tools for Apache Spark | killexams.com existent questions and Pass4sure dumps

    Microsoft has revamped its MMLSpark open source project, the better to integrate “many deep learning and data science tools to the Spark ecosystem,” according to the notes on the project repository.

    MMLSpark, originally released terminal year, is a collection of projects intended to accomplish Spark more useful in many contexts—mainly machine learning, but likewise in some general-purpose ways.

    Some of MMLSpark’s features integrate Spark with Microsoft machine learning offerings such as the Microsoft Cognitive Toolkit (CNTK) and LightGBM, as well as with third-party projects such as OpenCV. Others are about turning Spark into a service or client—for example, allowing Spark computations (including machine learning predictions) to subsist easily served via the web, or allowing Spark to interact with other web services via HTTP. One function, LIME on Spark, provides annotated results for the predictions served by a given image classifier, an at-a-glance passage to determine if the classifier is working right.

    MMLSpark wraps indecent these functions in a set of APIs available for both Scala and Python. The repository contains some quick-start examples, such as using web services in Spark, using OpenCV on Spark for image manipulation, and training a deep image classifier using Azure VMs with GPUs.

    MMLSpark itself can subsist installed on existing Spark clusters as a package, used in the Databricks cloud (or a Databricks appliance on Azure), installed directly in an instance of Python or Anaconda, or hasten in a Docker container. Integration is likewise available for the R language, but perquisite now only via a beta auto-generated wrapper.


    Who's charged in Data & Analytics: Top Vendors to Watch | killexams.com existent questions and Pass4sure dumps

    Organizations are using data to facilitate digital transformation, making data analytics a booming market. They highlight vendors that are addressing enterprise challenges with products for advanced analytics, machine learning, and data governance.

    2018 has been an necessary year for businesses across industries as they continue to digitize more of their operations. To carry out that well, they need good, character data to better the accuracy of their analytics and to train synthetic intelligence (AI) applications. 

    Many organizations hold been working difficult to liberate data trapped in systems because they need access the datasets. While the problem has not been solved completely, companies continue to unearth original insights that enable them to better control costs, create original revenue streams and enlarge profitability. 

    According to MicroStrategy's 2018 Global status of Analytics Report, which surveyed 500 analytics and industry intelligence professionals, 57% hold achieved faster and more efficient decision making through data and analytics use. Nearly two-thirds (64%) system to invest more in data and analytics talent going into the 2020s, and almost three in five (57%) already employ a chief data officer. The CDO role (or its equivalent) is now considered essential for overseeing everything associated with data, ranging from learning of data assets and their employ to articulating a strategy for using data to achieve competitive differentiation.

    Data privacy and security concerns touch use

    The journey to realize a more knowing enterprise is not without its challenges, however. This year, many enterprises had to scramble to ensure compliance with the European Union's universal Data Protection Regulation (GDPR) and are now facing the additional overhead that will subsist associated with the California Consumer Privacy Act, slated to evanesce into outcome in 2020. In the MicroStrategy survey, 49% said data privacy and security concerns are holding their organization back from using data more effectively. In addition, 45% reported that less than half of their organization's data is governed, a sobering statistic. The lack of solid governance exposes enterprises to potential privacy and security risks that need to subsist managed more effectively affecting forward. 

    Organizations are under tremendous pressure to understand the data assets they hold and hold access to, because without awareness of the inventory, the condition of data assets, data ownership, and the rules restricting use, enterprises hold a difficult time driving maximum value from their data and managing the potential risks and liabilities.

    AI requires a mindset shift

    Meanwhile, there is a major thrust to supplement data and analytics capabilities with AI. More vendors are touting their latest generation tools that may or may not actually remove advantage of AI, and more specifically, machine learning and deep learning. Sadly, enterprise buyers may subsist unable to discern the variation between some vendors' dubious claims and the actual capabilities of offerings because they lack a basic understanding of AI-related concepts. 

    In addition, there are risks associated with AI-powered systems that industry and IT leaders need to consider, such as the color that may subsist present in the datasets used to train those systems and -- in the case of deep learning -- the potential inability for the system to interpret the result and the reasoning that went into the result. In addition, Gartner estimates that by 2020, 50% of original data transformation flows will integrate one or more machine learning algorithms resulting in erroneous interpretations of data.

    Data character and metadata matter

    Data character is hardly a original concept, but the need for it is vital, not only to ensure more accurate analytics, but likewise to ensure that AI training data is reliable. Enterprises need to spot greater accent on data character out of necessity or suffer the consequences since "garbage in, garbage out" can erode brand reputation and customer trust, or worse. AI requires lots of training data. If the data used to train the system is of needy quality, it negatively affects the reliability of the insights and recommendations the system renders. 

    Given the sheer volume of data, increasing regulation and the data-intensive competitive environment, organizations need metadata management tools to enable effectual data governance, data management and regulatory compliance. Metadata management is essential for understanding data and likewise for recognizing necessary patterns in data. It's likewise necessary to achieve effectual data security and target marketing. According to Gartner, by 2020, 50% of information governance initiatives will subsist enacted with policies based on metadata alone.

    Our top 30 vendors

    It's tough to choose just a sampling of vendors out of thousands when driving value from data requires so many tools. The following three categories of vendors were selected based on current market trends and their faculty to address current and emerging enterprise requirements at scale. Note that some vendors may hold products in more than one category, but are only listed once to allow us to concede the leadership of a greater number of companies. Check out the charged vendors in other segments of IT in their full Vendors to Watch package.

    Advanced Analytics

    Birst (an Infor company; @BirstBI) offers a cloud-native BI, analytics and data visualization platform for the enterprise.

    IBM (@IBMAnalytics) providesan end-to-end ecosystem of data, analytics and cognitive capabilities.

    MicroStrategy (@MicroStrategy) markets a unified platform for enterprise analytics and mobility.

    Oracle (@Oracle) offers enterprise SaaS application suites, database PaaS and IaaS. Its analytics platform provides visual analysis, discovery advanced analytics, reporting, and forecasting capabilities.

    Qlik (@qlik) markets a platform for self-service data visualization, reporting and guided and embedded analytics.

    SAP (@SAP) offers analytics products including a cloud platform, databases, data warehousing, broad data, BI and advanced analytics.

    SAS (@SASsoftware) is an established vendor providing analytics, BI and data management software and services.

    Sisense (@Sisense) sells BI and analytics tools that allow users to easily prepare, resolve and explore growing data from multiple sources.

    Tableau Software (@tableau) is well-known for its data visualization capabilities, offering enterprise, cloud and embedded analytics.

    ThoughtSpot (@thoughtspot) provides a industry intelligence and broad data analytics platform for exploring, analyzing, and sharing real-time industry analytics.

    Data Science and Machine Learning Platforms

    Alteryx (@alteryx) enables self-service BI, data preparation, data blending and advanced analytics.

    Anaconda (@anacondainc) is the most Popular Python data science platform, available as an open source project and an enterprise product.

    Amazon Web Services (@awscloud) offersa broad array of products and services for processing, analyzing and visualizing data easily and cost-effectively. It likewise offers several AI products and services spanning frameworks and infrastructure, API-driven services and machine learning platforms.

    Databricks (@databricks) provides a platform for data science and engineering that handles indecent analytics processes from ETL to models training and deployment. 

    DataRobot (@DataRobot) offers a machine learning automation platform that allows anyone to build and deploy accurate predictive models, fast.

    HAL24K (@Hal24k) is an intelligence lab that offers modeling, analysis and visualization through its SaaS-based platform.

    H20.ai (@h2oai) is an open source data science and machine learning platform.

    KNIME (@knime) provides open source software for creating data science applications and services.

    Microsoft (@Azure) offers the Azure platform, which includes a broad sweep of data and analytics, machine learning, and cognitive computing products and services.

    RapidMiner (@RapidMiner) markets a data science platform that combines data preparation, machine learning and predictive model deployment.

    Data Management and Governance

    Advanced Metadata (@AdvMetadata) enables enterprises to understand their data through solutions including data landscaping, data character and data analytics.

    Anonos (@anonos) provides data management, security, and privacy solutions.

    BigID (@bigidsecure) helps organizations secure their customer data and meet privacy regulations.

    Cloudera and HortonWorks (@cloudera; @hortonworks), which announced plans to combine their companies as leading distributors of broad data platforms based on open source Apache Hadoop.

    D-ID (@D_ID_) is a startup marketing a solution that protects organizations' photos and videos from facial recognition.

    Innosec (@InnoSecCyRisk) provides automated tools for managing GDPR compliance and performing privacy repercussion and risk assessments.

    MarkLogic (@MarkLogic) markets an operational and transactional enterprise NoSQL database.

    Talend (@Talend) offers a data management platform that includes data integration and data character capabilities.

    Teradata (@Teradata) is a major provider of databases, data warehousing and analytics platforms.

    TIBCO Software (@TIBCO) providesdata management that enables consistent accessibility, delivery, governance, and security of data to meet an organization's requirements.

    Learn about more charged vendors in their full Vendors to Watch package.

    (Cover image: Billion Photos/Shutterstock)

    Lisa Morgan is a freelance writer who covers broad data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist knowing Unit. Frequent areas of coverage involve ... View plenary Bio We welcome your comments on this topic on their companionable media channels, or [contact us directly] with questions about the site.

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