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70-774 | Perform Cloud Data Science with Azure Machine Learning?

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70-774 - Perform Cloud Data Science with Azure Machine Learning? - braindump

Vendor Microsoft
Exam Number 70-774
Exam Name Perform Cloud Data Science with Azure Machine Learning?
Questions 37 Q & A
Recent Update November 12, 2018
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70-774 exam Dumps Source : Perform Cloud Data Science with Azure Machine Learning?

Test Code : 70-774
Test Name : Perform Cloud Data Science with Azure Machine Learning?
Vendor Name : Microsoft
Q&A : 37 Real Questions

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Microsoft Microsoft Perform Cloud Data

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

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

impartial software dealer (ISV) applications like ANSYS CFD are on the heart of CFD simulations. Microsoft Azure “huge Compute” can deliver heaps of cores on demand to ANSYS consumers to run their complicated CFD simulation initiatives. These simulations should be 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 large customer to run ANSYS CFD workload on Azure,” pointed out Ray Milhem, vice chairman of business 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 way of the pleasant folks at Microsoft.


Microsoft says future facts centres could be underwater | killexams.com Real 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 put a server pod underwater off the coast of California.

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

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

Deploying servers within the ocean potential they can also be close 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 speed at which servers may also be deployed the usage of this underwater method.

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

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

Alteryx Now permits records Analysts To function In-Database blending In Microsoft Azure SQL facts Warehouse | killexams.com Real 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 area with Microsoft Azure SQL statistics Warehouse. This aggregate provides analysts with a higher solution to take 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 delay to Microsoft power BI for wider consumption across the business.

“Alteryx now makes it even less demanding for data analysts to operate big 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 have Alteryx execute the tactics in the Microsoft Azure cloud atmosphere earlier than pushing the statistics to Microsoft power BI to imagine every little thing in one vicinity.”

“we've seen a growing to be number of analysts the use of in-database blending to speed up their time to perception,” talked about Tiffany Wissner, Senior Director of statistics Platform advertising at Microsoft. “Now, greater users can take 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 ability to at once and simply blend data from the database with different facts sources while the use 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 increase their statistics mixing efficiency via moving 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 enormous scale mixing and advanced analytics with out the need to know or use SQL coding.
  • Output to Microsoft energy BI – ultimately during the existing Alteryx and Microsoft relationship, analysts can make the most of the Microsoft stack and push their blended and analyzed dataset at once to vigor BI to create prosperous visualizations, dashboards, and experiences to share deeper insights inside their company.
  • more information on Alteryx and Microsoft can also be found at http://www.alteryx.com/microsoft. Alteryx offers a Starter package for Microsoft with analytics examples to support consumers get began. This equipment may also be downloaded at http://www.alteryx.com/microsoftkit.

    additionally, be part of Alteryx and Microsoft for a Webinar on can also tenth featuring Azure SQL records Warehouse and Alteryx integration to be 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 analyze all of their records using a repeatable workflow, then install and share analytics at scale for deeper insights in hours, no longer weeks. Analysts love 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 be a part 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. discuss with www.alteryx.com or name 1-888-836-4274.

    Alteryx is a registered trademark of Alteryx, Inc. All different product and brand names can be 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 Name : Perform Cloud Data Science with Azure Machine Learning?
    Vendor Name : Microsoft
    Q&A : 37 Real Questions

    Dont forget to strive these latest dumps questions for 70-774 exam.
    Thankyou killexams..I have cleared my 70-774 examination with ninety two%. Your question bank turned into very beneficial. If all peoplepractices 100% in reality from your query set and studies all of the questions nicely, then he is going to definately be successful. Until now i have cleared three other checks all with the assist of your website. Thanks once more.


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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 real 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 last year, is a collection of projects intended to make Spark more useful in many contexts—mainly machine learning, but also 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 be 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 way to determine if the classifier is working right.

    MMLSpark wraps all 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 be 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 run in a Docker container. Integration is also available for the R language, but right now only via a beta auto-generated wrapper.


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

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

    2018 has been an important year for businesses across industries as they continue to digitize more of their operations. To do that well, they need good, quality data to improve the accuracy of their analytics and to train artificial intelligence (AI) applications. 

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

    According to MicroStrategy's 2018 Global State of Analytics Report, which surveyed 500 analytics and business intelligence professionals, 57% have achieved faster and more efficient decision making through data and analytics use. Nearly two-thirds (64%) plan 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 knowledge of data assets and their use to articulating a strategy for using data to achieve competitive differentiation.

    Data privacy and security concerns affect use

    The journey to realize a more intelligent enterprise is not without its challenges, however. This year, many enterprises had to scramble to ensure compliance with the European Union's General Data Protection Regulation (GDPR) and are now facing the additional overhead that will be associated with the California Consumer Privacy Act, slated to go into effect 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 be managed more effectively moving forward. 

    Organizations are under tremendous pressure to understand the data assets they have and have access to, because without awareness of the inventory, the condition of data assets, data ownership, and the rules restricting use, enterprises have 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 push to supplement data and analytics capabilities with AI. More vendors are touting their latest generation tools that may or may not actually take advantage of AI, and more specifically, machine learning and deep learning. Sadly, enterprise buyers may be unable to discern the difference 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 business and IT leaders need to consider, such as the bias that may be present in the datasets used to train those systems and -- in the case of deep learning -- the potential inability for the system to explain the result and the reasoning that went into the result. In addition, Gartner estimates that by 2020, 50% of new data transformation flows will integrate one or more machine learning algorithms resulting in erroneous interpretations of data.

    Data quality and metadata matter

    Data quality is hardly a new concept, but the need for it is vital, not only to ensure more accurate analytics, but also to ensure that AI training data is reliable. Enterprises need to place greater emphasis on data quality 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 poor 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 effective data governance, data management and regulatory compliance. Metadata management is essential for understanding data and also for recognizing important patterns in data. It's also necessary to achieve effective data security and target marketing. According to Gartner, by 2020, 50% of information governance initiatives will be 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 ability to address current and emerging enterprise requirements at scale. Note that some vendors may have products in more than one category, but are only listed once to allow us to acknowledge the leadership of a greater number of companies. Check out the hot vendors in other segments of IT in our 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, big 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, analyze 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 business intelligence and big data analytics platform for exploring, analyzing, and sharing real-time business 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 also 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 all 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 range 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 quality and data analytics.

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

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

    Cloudera and HortonWorks (@cloudera; @hortonworks), which announced plans to combine their companies as leading distributors of big 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 impact 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 quality 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 hot vendors in our full Vendors to Watch package.

    (Cover image: Billion Photos/Shutterstock)

    Lisa Morgan is a freelance writer who covers big 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 Intelligent Unit. Frequent areas of coverage include ... View Full Bio We welcome your comments on this topic on our social media channels, or [contact us directly] with questions about the site.

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    H2O.ai's Full Suite of AI Platforms Now Available in the Microsoft Azure Marketplace | killexams.com real questions and Pass4sure dumps

    No result found, try new keyword!In February 2018, Gartner named H2O.ai, a Leader in the 2018 Magic Quadrant for Data Science and Machine Learning Platforms. H2O.ai partners with leading technology companies such as NVIDIA, IBM, AWS, ...


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    References :


    Box.net : https://app.box.com/s/rgdfzj17r0afagoxp3hbano0qh7gl8lb
    zoho.com : https://docs.zoho.com/file/67jzb130ea4a6eb2d4d519e23d0c9e4051ae9
    Calameo : http://en.calameo.com/books/0049235263ed8d0eda28e






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