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

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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
: 37 Real Questions

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Microsoft Perform Cloud Data Science

HubStor Advances Cloud data administration With identification Intelligence | killexams.com Real Questions and Pass4sure dumps

KANATA, Ontario, Feb. 19, 2019 /PRNewswire/ -- HubStor these days announced new cloud facts management capabilities that permit organizations to make use of Microsoft Azure energetic directory's extended identity attributes in guidelines that control the storage, renovation, and safety of unstructured facts.

The HubStor cloud data management platform uniquely protects unstructured data workloads while incorporating a question-optimized mapping of data entry rights, clients, and neighborhood memberships. Now with prolonged identification metadata correlated into HubStor's intelligent policy engine, organisations can streamline their administration of critical counsel in the following approaches:

  • Storage: automatic enrollment into secondary storage – HubStor can now dynamically detect when a user leaves the company and instantly join their Microsoft office 365 mailbox and OneDrive for business site in a backup/archiving coverage that captures the records into Azure-based mostly secondary storage. 
  • renovation: Retention or prison cling automation – HubStor can dynamically set retention intervals on a person's information in secondary storage when, as an example, the person joins a department or leaves the organization. 
  • security: position-based mostly entry handle for statistics streams – HubStor can hinder entry permissions to global records streams in order that privileged users can search, access, or recover assistance concerning specific units of clients most effective. HubStor's new potential to automate a logical separation within a single statistics move for function-based access control is gaining traction in the following situations: 
  • electronic mail journaling – Storage suggestions in HubStor leverage the Organizational Unit attribute to control entry to messages in order that felony discovery users can conduct searches, practice holds, and function exports on e mail custodians inside their particular area only, for example.
  • office 365 audit log retention – similarly, HubStor's storage guidelines can leverage the branch attribute to create a logical separation of audit log statistics in order that selected IT and security directors can evaluate and produce workplace 365 event heritage for users inside particular departments or workplace locations handiest.
  • "We listen to the wants of their valued clientele intently as they build out the HubStor cloud data management platform," talked about Brad Janes, VP of Product management at HubStor. "improving HubStor's integration with Azure energetic listing and the HubStor policy engine to include id metadata unlocks never-before-viewed data administration capabilities in the IT trade." 

    which you can join with HubStor to start a subscription here: https://www.hubstor.web/installation-now.

    About HubStor  

    HubStor is a number one innovator in cloud-primarily based storage utility. companies use the HubStor cloud data administration platform to radically change their facts storage and insurance policy practices, backup their office 365 statistics, journal digital messages, permit cloud-tiering of file programs, and control long-term retention of unstructured statistics. HubStor is headquartered in Ottawa, Canada, and is a Microsoft Co-sell Prioritized and Gold ISV associate.  

    Media Contact

    Elizabeth Lam, VP advertising and marketing

    liz@hubstor.net

    source HubStor Inc.

    connected links

    http://www.hubstor.internet


    business machine researching route accelerates Your course to essentially the most fashionable Certification in facts Science | killexams.com Real Questions and Pass4sure dumps

    No outcome found, are trying new key phrase!... extra really good information science certifications from different cloud suppliers, as smartly. "Azure records Scientists follow Azure's computing device getting to know thoughts to coach, evaluate, and installation fashions that solve ...

    independent automobiles, massive records, and area Computing: What You need to recognize | killexams.com Real Questions and Pass4sure dumps

    this text is featured within the new DZone book to big records: quantity, diversity, and speed. Get your free copy for insightful articles, trade stats, and greater!

    The driverless automobile has been a high-tech dream for many years. Now that broadband connectivity, cloud computing, and artificial intelligence are increasingly available, independent vehicles may still go mainstream in the close future, offered certain technical and regulatory milestones are reached. but another situation that need to be addressed before self-riding vehicles can attain crucial mass is the situation of facts. above all, the facts analysis and storage necessities of autonomous vehicles latest challenges past the capabilities of most existing big information options.

    autonomous cars generate a striking quantity of facts. Intel estimated one vehicle generates terabytes of information in eight hours of operation. distinctive photographs, radar/lidar, time-of-flight, accelerometers, telemetry, and gyroscope sensors generate data streams that ought to be analyzed with the intention to perform the calculations and adjustments required to soundly navigate a car. That analysis needs to happen in precise-time if the vehicle is to sustain with invariably changing using conditions (other vehicles or pedestrians relocating across the car, altering weather and light-weight conditions, traffic signals, and the like). These true-time performance necessities suggest there is no time to upload information to a critical server, behavior the necessary analytics, after which send directions back to the vehicle for execution. records that's vital to soundly navigate the motor vehicle have to be analyzed in the neighborhood via the car itself — pretty much, the car is an side machine in a cloud community.

    no longer handiest does the motor vehicle should analyze statistics by itself, it ought to additionally learn to prefer and judge between distinctive facts streams to establish those gold standard ideal for evaluation at any given second to hold the car driving safely. 

    That last requirement — the need to determine what facts is required to function an analysis — is tricky. whereas predefined filters can help a motor vehicle's computing device getting to know routines be taught what statistics to use and when to use it, these filters are generated by means of human engineers, so that they can not be up to date in precise-time. as a consequence, an autonomous automobile will need to run computing device discovering and analytics engines potent adequate to appreciate mission-important facts requiring immediate evaluation and action on their own, with out involving a human in the evaluation. once input from a person is required, resolution-making in line with information evaluation in true time is without problems not possible.

    We want analytics and machine getting to know algorithms for autonomous automobiles that can:

  • establish information in all formats.

  • respect what records is required for mission-critical operations and function analysis of that records in the neighborhood.

  • Compress or aggregate non-important facts for importing to the cloud for future use.

  • schedule uploads of non-vital facts from the car to the cloud when much less high priced communications are available (as an instance, when the motor vehicle is parked overnight at domestic and may access the owner's Wi-Fi in its place of a metered cellular community).

  • be aware of the way to demand ancient information from the cloud so the AI can use it for future analytics.

  • The remaining bullet is above all crucial. An self sufficient automobile company can be responsible for storing mammoth amounts of information generated through vehicles operating everywhere, and a good deal of that data will probably don't have any actual cost when at the beginning captured. although, that facts's value may be published in the future as the manufacturer's self sustaining using applications evolve and enhance. brand new non-important facts can also be advantageous for future purposes, provided the records is correctly kept and simply purchasable. if they don't make plans in develop for the way to make information accessible every time indispensable, self sufficient vehicle vendors run the risk of creating a "dark data" issue. darkish records is the term used to explain data property a firm collects but fails to take capabilities of — as a result of they do not know a way to, or most likely forgot they've. This can be a particularly large difficulty for self-riding automobiles as a result of the sheer volume of data they generate.

    To handle the darkish data issue, self sufficient automobile providers need to circulate their statistics storage recommendations away from data warehouse models and adopt emerging data storage fashions like facts lakes. while an in depth examination of the difference between a knowledge warehouse and an information lake is past the scope of this article, as an example the change between the two, evaluate a e-book with a library. With a ebook (records warehouse), a person has already determined what content is contained in that e-book and the way it's formatted, while a library (records lake) allows you to save some thing content you desire in almost any structure. In other words, an information warehouse is a centralized platform for primary importing, exporting, and preprocessing of records gathered from a group of linked programs the use of one statistics schema. an information lake is a distributed yet integrated information platform that helps schemaless (together with unstructured and structured) statistics and performs queries of statistics in true-time by using leveraging metadata to without delay find, seriously change, and cargo information between systems. data lakes' guide for each structured and unstructured facts on the same platform is vital, as self reliant motor vehicle sensors generate datastreams in very distinctive codecs that can't without difficulty be kept within the identical schema. other key ameliorations that distinguish a knowledge lake from a data warehouse include:

    Linking statistics between clusters is certainly important for self sustaining cars, as it makes it possible for for the mixing of distinct datasets from diverse geographic places. motor vehicle OEMs are global agencies with multiple places of work and statistics facilities scattered around the world. As more nations stream to assist autonomous automobiles, independent motor vehicle vendors will want to use all the facts generated by vehicles using locally in the self-riding AI and ML algorithms they use to power their automobiles globally. As they see more companies enter the autonomous using market, the ones who will sooner or later win out over others could be those vendors optimum prepared to investigate statistics at the native stage and those who have cataloged their databases accurately — so future self sustaining functions can discover the information they need, once they want it.

    this text is featured in the new DZone e-book to huge facts: extent, variety, and speed. Get your free replica for insightful articles, industry stats, and greater!

    Bias comes in a number of types, all of them doubtlessly damaging to the efficacy of your ML algorithm. Their Chief data Scientist discusses the supply of most headlines about AI failures here.

    subject matters:

    huge records ,autonomous cars ,actual-time statistics analysis ,laptop researching


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    Perform Cloud Data Science with Azure Machine Learning?

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    Enterprise Machine Learning Course Accelerates Your Path to the Most Sought After Certification in Data Science | killexams.com real questions and Pass4sure dumps

    No result found, try new keyword!We look forward to seeing more specialized data science certifications from other cloud providers, as well. "Azure Data Scientists apply Azure's machine learning techniques to train, evaluate, and dep...

    Review: Azure Machine Learning challenges Amazon SageMaker | killexams.com real questions and Pass4sure dumps

    Azure Machine Learning Service is Microsoft’s latest offering for developers and data scientists in the custom cloud machine learning and deep learning category. Azure Machine Learning Service adds to a suite of Azure AI products that includes numerous AI toolkits, chatbot and IoT edge services, data science VMs, and pre-built services for vision, speech, language, knowledge, and search.  

    editors choice award logo plumInfoWorld

    The AI toolkits include Visual Studio Code Tools for AI, the older drag-and-drop Azure Machine Learning Studio, MMLSpark deep learning tools for Apache Spark, and the Microsoft Cognitive Toolkit, previously known as CNTK, which is being de-emphasized in favor of other machine learning and deep learning frameworks.

    Using cloud resources for training deep learning models makes eminent sense in many cases. Using the cloud for training doesn’t necessarily replace the convenience and low operating cost of using your own computer for model building, especially if you have one with lots of RAM and a capable GPU such as an Nvidia Titan RTX. On the other hand, using the cloud offers the opportunity to add compute resources as needed, potentially reducing the time it takes to complete your experiments and find a sufficiently accurate predictive model.

    All of the major cloud services now offer machine learning and deep learning development environments. On AWS, that’s primarily Amazon SageMaker, which I reviewed in May 2018. On the Google Cloud Platform, that’s primarily Cloud Machine Learning Engine and the beta Cloud AutoML. On the IBM cloud, that’s primarily IBM Watson Studio. I’ll compare Azure Machine Learning Services with Amazon SageMaker later in this review.


    The ‘Big Bang’ of Data Science and ML Tools | killexams.com real questions and Pass4sure dumps

    The tools used for data science are rapidly changing at the moment, according to Gartner, which said we’re in the midst of a “big bang” in its latest report on data science and machine learning platforms.

    “The data science and ML market is healthy and vibrant, with a broad mix of vendors offering a range of capabilities,” Gartner says in its Magic Quadrant for Data Science and Machine Learning Platforms published January 28. “The market is experiencing a ‘big bang’ that is redefining not only who does data science and ML, but how it is done.”

    The analyst group defines a data science platform as an integrated place where data scientists, citizen data scientists, and developers can get all of the core capabilities that they need to not only build data science application, but to embed them into existing business processes and manage and maintain them over time.

    Data science and ML platforms must meet minimum requirements, and include tools for

  • ingesting and preparing data;
  • interactively exploring and visualizing data;
  • engineering data features and building predictive models;
  • testing and deploying those models in an integrated fashion with surrounding infrastructure.
  • Gartner Magic Quadrant for Data Science and Machine Learning Platforms (Source: Gartner)

    Integration and cohesion are keys, in Gartner’s view, and applications that simply bundle various packages and libraries – especially open source offerings — are not considered true platforms.

    While these core requirements set the stage for data science and ML platforms, there are big differences in how the various suppliers get there. Gartner notes that expert data scientists may prefer writing code in Python or R, while others like the ease of use of data science notebooks, such as Jupyter. Still other less technical folks prefer more intuitive point and click interfaces.

    Leader’s Quadrant

    Gartner placed four vendors in the Leader’s Quadrant, including KNIME, RapidMiner, TIBCO Software, and SAS.

    KNIME ranked highly in Gartner’s assessment as a result of strong support from customers, a broad product set, and having “one of the most balanced” visions in the market. The Zurich company’s product lineup – which consists of the open source KNIME Analytics offering and the commercial KNIME Server product — were lauded as the “Swiss Army Knife” of analytics. Support for advanced features like deep learning, ease of use by intermediate users, and integration with other packages were lauded. However, performance and scalability were seen as weaknesses, as well as limited traction in IoT.

    Rapid Miner also ranked highly in the leader’s quadrant thanks to its balance between ease of use and supporting sophisticated data science capabilities. The software supports deep learning technology and deploys to GPUs, and Gartner seemed to like how Rapid Miner’s delivers more transparency for machine learning deployments. Its integration with open source tools will be beneficial to data scientists, it says. The main concerns are around data prep and visualization; licensing and pricing; and model operationalization.

    TIBCO made a big move up from the Challenger’s Quadrant by purchasing a range of analytics properties, including Jaspersoft, Spotfire, Statistica, and Alpine Data, and integrating them into a single cohesive platform. Gartner liked the end-to-end workflow integration that TIBCO delivers, and its IoT capabilities – particularly with the integration of streaming analytics. Potential concerns include performance and stability, data management, and questions around operationalization.

    SAS is a perennial contender on this list, and in fact has multiple platforms that were assessed. Its Enterprise Miner offering delivers strong, reliable performance across a range of metrics, while Visual Data Mining and Machine Learning (VDMML) had high scores for data prep and augmentation. High customer satisfaction levels and strong market presence bolster SAS’s position as a leader. But Gartner also listed some downsides of SAS’s approach, particularly around pricing and product coherence. The SAS EM user experience hasn’t kept up with expectations, and SAS’ approach to open source is a question mark for Gartner.

    Challenger’s Quadrant

    The Challenger’s Quadrant was fairly empty, with just Alteryx and Dataiku occupying that space.

    Alteryx dropped from the Leader’s Quadrant by maintaining its “ability to execute” (the Y axis) but losing some of its “completeness of vision” (the X axis). Gartner heralded the Irvin, California company’s citizen data science capabilities within an end-to-end pipeline. Despite its capabilities, the market perceives Alteryx as just a data preparation tool, which obscures its value, the analyst group says.

    Dataiku‘s Data Science Studio (DSS) offering received high marks for the way it fosters collaboration among different stakeholders, from data engineers to scientists. Gartner also liked the automation it brings to the machine learning workflow, as well as the management and monitoring of models once they’re in production. Some concerns include scalability, pricing, and support for streaming analytics and IoT use cases, it says.

    Visionaries Quadrant

    The Visionaries Quadrant was crowded, with new fewer than seven vendors jockeying for position.

    Databricks, which inked $250 million in venture funding this week, impressed Gartner with its support for the full analytics life cycle, its support for hybrid cloud strategies, and its capability to support a variety of users. Users spoke highly of the Spark-based cloud offering, and documentation was a plus, per Gartner. Pricing and contract negotiations were potential weak spots for Databricks, along with monitoring, management, and troubleshooting and debugging potential problems.

    DataRobot debuted on the quadrant in the Visionaries, thanks to the fact that it “sets the standard for augmented data science and ML,” Gartner says. Customers enjoy a “strong experience,” which is helping the company to gain traction with an already solid installed base. Sales execution, pricing, scalability concerns, and the possible commoditization of the “augmented analytics” space are cocerns.

    H2O.ai, which held its H2O World conference this week, dropped from the Leader’s Quadrant in 2019 into the Visionaries Quadrant as a result of strong competition, and some concerns from customers about capabilities. The performance of its core open source machine learning components remain a strength for H2O.ai, and Gartner was impressed with its GPU-based deep learning and the automated ML capabilities of Driverless AI. But a steep learning curve for non-developers, a lack of management capabilities, and a lack of data access and data prep features were concerns.

    MathWorks made a huge lateral move, from the Challengers to the Visionaries Quadrant, thanks to “a remarkable strength” in serving the demands of its customers in asset-centric industries, according to Gartner (the company has a long heritage among manufacturers and engineering organizations). Its MATLAB offering was hailed for its “citizen engineer” capabilities, and integrated data prep and support for real-time streaming, deep learning, and simulation impressed the G man. Dings were difficulty of use by non-engineers, no support for Google Cloud Platform, and a lack of automated machine learning capabilities were downsides.

    Microsoft scored well with its cloud-based offerings, which include Azure Machine Learning, Azure Data Factory, Azure HDInsight, Azure Databricks, and Power BI. Gartner liked how Microsoft works with third-parties, in particular Databricks’ Spark offering. Support for diverse data personas, including entry-level ML enthusiasts, was also a plus. Automation in the ML process was a concern, as was the coherence of all the different tools. A lack of on-prem capabilities also limits its applicability.

    IBM stays in the Visionaries Quadrant for 2019, but it has lost ground. Gartner praised the comprehensive nature of IBM’s Watson Studio offering, which serves expert and citizen data scientists. Integration of the SPSS modeler into Watson Studio was also praised. But the frequency that IBM rebrands products and shifts strategy is a concern to Gartner, as is the need to license multiple products to get complete end-to-end capabilities.

    Google did pretty well in the data science and ML platform ranking, thanks largely to the wide breadth of tools available on its cloud. Its core data science platform consists of Cloud ML Engine, Cloud AutoML, TensorFlow, and BigQuery ML. But Google also offers unique hardware, with the Tensor Processing Unit (TPU), crowdsourcing with Kaggle, and a range of other offerings. Scalability and speed are strengths. But a lack of end-to-end cohesion among the tools was a concern, as well as a lack of reusability. The lack of an on-prem offering was also a concern.

    Niche Players Quadrant

    Four vendors found themselves in the Niche Players Quadrant.

    SAP’s Predictive Analytics (PA) offering is tightly integrated with HANA, which makes it suitable for SAP HANA customers. The capability to process large HANA datasets and deploy models to SAP applications are strengths. So is SAP’s vision of a unified ML fabric, which is tied to its Leonardo Machine Learning Foundation. However, product coherence, a changing AI strategy, and the customer experience were marks against the German giant.

    Domino Data Lab was downgraded from the Visionaries Quadrant, which reflected mostly a drop in its perceived ability to execute. Gartner likes Domino’s product strategy, in particular its focus on collaboration and building an end-to-end solution. Its ability to integrate with open source and proprietary products was a bonus, as was its scalability. But Domino’s focus on expert data scientists leaves citizen data scientists wanting, according to Gartner, and it also lacks some data prep, automation, and augmentation capabilities.

    Anaconda remained in the Niche Players category. Key strength of the Anaconda product is its reach into the open source Python community, which continues to churn out data science innovation. Its capability to scale open source Python is also a plus. But the expertise needed to successfully wield the Anaconda platform is a caution, per Gartner, and the complexity of the Python “jungle” is also a concern. Reliance on the open source community also puts customers at a disadvantage when they need something specific (Gartner uses the example of model operationalization), and the overall level of coherence is a downside.

    Datawatch is a newcomer to this Magic Quadrant by way of its January 2018 acquisition of Angoss, which has more than 20 years of experience in the field. Gartner praised the coherence and ease of use of the Datawatch products, and marked the text analytics and optimization engine components as above average. Customer support was also a plus. A lack of data preparation capabilities dragged Datawatch’s score down, while the overall vision of the product and uncertainties raised by the acquisition were also mentioned.

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