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

Vendor Name : Microsoft
Exam Code : 70-774
Exam Name : Perform Cloud Data Science with Azure Machine Learning?
Questions and Answers : 37 Q & A
Updated On : October 17, 2018
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Microsoft 70-774 Exam (Perform Cloud Data Science with Azure Machine Learning?) Detailed Information

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


70-774 Test Objectives


Prepare Data for Analysis in Azure Machine Learning and Export from Azure Machine Learning
Develop Machine Learning Models
Operationalize and Manage Azure Machine Learning Services
Use Other Services for Machine Learning

70-774 Questions and Answers

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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
Q&A : 37 Real Questions

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Microsoft Microsoft Perform Cloud Data

challenge xCloud: Microsoft proclaims game-Streaming provider | killexams.com Real Questions and Pass4sure dumps

After listening to about the service at E3 2018, Microsoft (NASDAQ:MSFT) is now formally announcing mission xCloud.

a dark room: Project xCloud © provided by way of InvestorPlace venture xCloud

mission xCloud is Microsoft’s vision to create a video game streaming carrier with a purpose to allow game enthusiasts to play games from any place. this may consist of letting game enthusiasts stream video games to their PCs, drugs, smartphones and other instruments.

The idea here is that Microsoft is planning to leverage the attain it has with its Azure cloud information facilities to push project xCloud. These facts centers will standard include hives of Xbox One video online game consoles with a view to deal with enjoying the video game.

in accordance with Microsoft, it is working to beat probably the most enjoyable challenges that a video video game streaming service has. This comprises constructing new video encoding efforts to cut back lag. it is already trying out challenge xCloud out at one in every of its facts centers and it looks promising.

here’s the rub. while what Microsoft is trying to do sounds top notch on paper, there’s no telling how smartly it is going to operate in a true-world environment. a lot of america nonetheless doesn’t have speedy information superhighway speeds. This includes web from typical and instant providers. cases of lag may be damaging to the service and make it a tough promote to gamers.

On a good observe, task xCloud will support connecting an Xbox One controller to mobile devices by the use of Bluetooth. That’s a real blessing, as no gamer desires to play with touch controls. MSFT is still working on a dash reveal overlay for games, but I basically can’t see it getting tons use from gamers.

MSFT inventory was down 1% as of Monday afternoon.


iboss cloud integrates Microsoft Azure to extend protection for corporate statistics | killexams.com Real Questions and Pass4sure dumps

iboss has prolonged its cloud security offerings to be available as a Microsoft Azure inner most app. This makes it possible for agencies working information inside Azure to extend to iboss gateway protection with a purpose to offer protection to this data because it is accessed with the aid of any user from any equipment, anywhere.

Extending throughout Azure, iboss cloud gateway safety reduces community blind spots and increases normal protection posture with visibility (or a single unified view) into who is accessing information and the place it goes.

as the traditional community perimeter erodes and corporate statistics continues to extend and live across dissimilar clouds, iboss’ potential to lengthen throughout them is critical to ensure users have secure entry to their applications.

“we're excited to deliver iboss’ offerings to Microsoft Azure, now not only because it is a leading cloud platform, but because further and further clients are sending site visitors by way of their network with the rapid boom of Azure and Microsoft workplace 365,” said Paul Martini, CEO and co-founding father of iboss. “shoppers will now have the network protection service as close to the site visitors as feasible, giving clients the most reliable experience when accessing the cloud.”

Prasetha Warrier, predominant Product supervisor at Microsoft spoke of, “through its SaaS safety offering, iboss offers Microsoft Azure clients both the finished insurance plan they need nowadays and the pliability to scale as they grow for the long run. Azure presents fine capabilities from global companions comparable to iboss with options verified to work seamlessly for our clients.”


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BOSTON--(business WIRE)--iboss, a leader in comfortable cloud gateways, announced today that it has prolonged its cloud security choices to be obtainable as a Microsoft Azure private app. This allows for organizations running statistics inside Azure to extend to iboss gateway protection to be able to offer protection to this records as it is accessed via any consumer from any device, any place.

Extending throughout Azure, iboss cloud gateway protection reduces community blind spots and raises usual safety posture with unmatched visibility (or a single unified view) into who's getting access to statistics and the place it is going. because the normal community perimeter erodes and corporate facts continues to extend and reside across diverse clouds, iboss’ skill to lengthen across them is important to be sure users have protected access to their applications.

“we're excited to bring iboss’ choices to Microsoft Azure, no longer simplest because it is a number one cloud platform, but as a result of further and further valued clientele are sending traffic by the use of their network with the fast boom of Azure and Microsoft office 365,” referred to Paul Martini, CEO and co-founder of iboss. “purchasers will now have the network safety service as near the traffic as viable, giving users the most efficient experience when gaining access to the cloud.”

Prasetha Warrier, predominant Product manager at Microsoft Corp. said, “via its SaaS protection offering, iboss gives Microsoft Azure customers both the finished insurance policy they need today and the pliability to scale as they grow for the long run. Azure offers pleasant features from global companions similar to iboss with solutions verified to work seamlessly for our customers.”

About iboss

developed for the cloud, iboss provides relaxed access to the internet for any user, on any gadget, from anyplace—all without the need for home equipment. iboss is backed by greater than 100 patents and patents pending and protects over 4,000 companies worldwide. one of the vital quickest becoming cybersecurity businesses on the earth, iboss believes in a world where each enterprise and each grownup shares the identical elite degree of security. To gain knowledge of more, discuss with www.iboss.com.


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

Study Guide Prepared by Killexams.com Microsoft Dumps Experts


Killexams.com 70-774 Dumps and Real Questions

100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



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

Feeling difficulty in passing 70-774 exam? you got to be kidding!
Yes, the question bank is very beneficial and I suggest it to every body who wishes to take these tests. Congrats on a job nicely idea out and executed. I cleared my 70-774 exams.


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Facebook launches PyTorch 1.0 with integrations for Google Cloud, AWS, and Azure Machine Learning | killexams.com real questions and Pass4sure dumps

Facebook today announced the release of deep learning framework PyTorch 1.0 in developer preview, which includes a series of tools and integrations to make it more compatible with popular services from Google Cloud, Amazon Web Services, and Microsoft’s Azure Machine Learning.

Arm, Nvidia, Qualcomm, and Intel are also adding PyTorch support for things like kernel library integrations and tools to track inference runtime.

PyTorch was released to the public in January 2017 and has been downloaded more than 1 million times.

PyTorch 1.0 was first announced in May at the F8 developer conference, and includes deeper integration with Facebook’s Caffe2 and ONNX.

Back in May, Facebook VP Bill Jia and CTO Mike Schroepfer promised PyTorch 1.0 would launch with new pretrained models, tools, and libraries to give developers more flexibility and options.

To meet those needs, Google Cloud will introduce PyTorch support for a number of its services. Google and Facebook are also working together to make tensor processing units (TPU) for PyTorch users, Facebook said in a blog post today announcing the news. Google Cloud Platform’s Deep Learning VM service for preconfigured AI will also include a VM image for PyTorch 1.0.

Amazon’s SageMaker will provide PyTorch users with preconfigured environments for things like automated model tuning.

Microsoft, which first partnered with Facebook a year ago on the development of the ONNX open source framework to make AI models created with different frameworks more portable, will also introduce several new resources for developers using PyTorch.

“Azure Machine Learning service now allows developers to seamlessly move from training PyTorch models on a local machine to scaling out on the Azure cloud. For data science experimentation, Microsoft is offering preconfigured Data Science Virtual Machines (DSVM) that are preinstalled with PyTorch. For developers looking to start exploring PyTorch without having to install software and set up a local machine, Azure Notebooks provides a free, cloud-hosted Jupyter Notebooks solution set up with PyTorch tutorials,” Facebook product manager Joe Spisak said today in the blog post.

Udacity will also introduce free online courses on how to use PyTorch, and fast.ai today released fastai 1.0, an open-source deep learning library built on top of PyTorch.

PyTorch version 1.0 rolls out in developer preview as part of the first-ever PyTorch developer conference, held today in San Francisco.


Can your business use Machine Learning without data? | killexams.com real questions and Pass4sure dumps

Using Andrew Ng’s mantra about spaceship and fuel it needs, anyone on a mission to create a first-class AI-powered product needs vast amounts of data to feed to the machines. Such a necessity is dictated by multiple parameters in models that computers are to master. And to tell the truth, the problem of getting a bunch of training samples or marked and structured information is even harder to solve than just take some algorithm and use it. However, we at PerceptionBox, want to share some unexpected data sources with you and explain how you can use machine learning to improve your business.

Data Sources

Depending on the sophistication of the problem, the number of parameters and the amount of data needed is big, too. However, being creative and going about costly big data is a way to go. By now you might be curious how to find data sets required for training the machines if not using a standard notion of feeding machine big data that, if eg scientific or medical records, can be a cost barrier? Here’s what we have to say in this regard.

And although often you cannot use the machine learning without any data at all, however, there are several general ways how ML&AI can power up your business:

  • Find the required data among multiple free datasets on the web that is necessary for usual machine learning system development. There a bunch of places where you can get a dataset that could be very useful for your business either for some ML system training or for classic data analytics. Check Google Dataset Search, Kaggle, Government Data or any other source.
  • If you require some industry-specific information for your machine learning algorithms training, but you may find crumbs of this priceless information on multiple web pages and websites, then you just can scrap it.There are really a lot of different cloud and on-premise tools like Octoparse, ParseHub, Dexi.io or others, that can help you do it fast, easy and automatically.
  • Plenty of ready-to-use ML solutions are at your disposal. You can easily enhance your business with multiple high-end technologies like speech recognition, computer vision, recommendation, and matching engines, etc.
  • If none of these ways didn’t help you, then deep learning with several special technics come into play.
  • Unpretentious Deep Learning

    Depending on the project focus, industry and detailed requirements, slightly different technologies or algorithms are preferable, but it stays up to your machine learning developers. But its general recommendation to use the latest and the most advanced algorithms of Deep Learning. Lower there is a bit modified slide from the above mentioned Andrew Ng that shows the reason why Deep Learning is so popular lately.

    After we looked at inputs to start a project with limited amounts of data at hand, let us further deep dive into alternative methods to work on the data you have:

    To capture long-term dependencies, one might consider transfer learning to start from. Let us assume you have a task to solve that is highly specific a certain domain and thus the number of data sets necessary for the project are nowhere to find. So in order to build new training models, you’d require additional resources which is costly.

    However, think of the models used in previous projects but share the specific domain component. Why not employing those in the project you deal with now? Such a method is widely known as transfer learning. Think of knowledge hand over-take over from a person resigning the company to a new associate.

    “Transfer learning is an up-and-coming technique that allows us to transfer the knowledge learned in one dataset and apply it to another dataset.”

    Bradley Arsenault

    Similar to training models, a fresher will have a foundation inherited from a senior associate leaving the company to build upon the future knowledge and concatenate them.

    If handling uncertainty caused by a small amount of training data, domain expertise and unsupervised learning (clustering) can be the keys. For example, having a hypothesis, you are still not sure about the final outcome of the research. So the target features are not provided in the training samples. The idea then is to build a classification that can be used to classify the raw data. The basics behind the unsupervised learning are finding the underlying structure of a dataset and grouping the raw material. These are the grounds of unsupervised learning that starts with unlabeled data.

    Think programmatic ads here and imagine that a marketing professional needs to judge the historical records of purchasing patterns of a community (or even a nation). An ad engine would cluster those that splurge on shoes on the one hand and others that drop at the organic store every weekend to shop healthy whole grained foods on the other hand. In the end, marketing gurus get better insights and can decide on targeting the ad campaigns better.

    Clustering is the essence here and it’s about putting events, people or things together depending on their relativity to each other with the clusters being used as insights to feed the machines.

    Finally, you might also look into reinforced learning. The latter is about trial and error, thus the efforts leading to self-discovery in an interactive environment when feedback from prior actions used as a guide and mapping. Employing the technique of learning from experience, a set of sticks and carrots helps machines refine the approach and decide on the best possible step to take next.

    Reinforced learning is the greatest step towards the machine learning future where ML systems are trained not by humans, but other ML systems.

    To illustrate the concept, we can look at the chemical lab setting and the way scientists took up optimizing chemical reactions with deep reinforcement learning to the next level. The core idea of the study undertaken by Stanford University was to create a model that would iteratively record the results of a chemical reaction and chooses new experimental conditions in order to enhance the reaction outcome.

    Right of the bat

    What if you decide to build up your strength on already existing training material available from tech giants and data enthusiast? Let us see what’s there for you.

    Google Cloud AI

    From cloud video intelligence to cloud translation API — that is the Google Cloud AI products your business can be enhanced with. Google is the company that has a lot of data to train its ML systems. That is why if you want to play hard you may take a glance on Google machine learning services to bring unmatched scale and speed to your business.

    Microsoft Azure AI Platform

    Microsoft Azure offers a variety of AI-powered cognitive services, that are able to bring into your business a lot of magic like abilities as complex information and data mapping, speech and text recognition, sentiment evaluation, etc. Pay as you go model let you try and pay only for what you use, while you scale your business on the go.

    Amazon Machine Learning

    Serving the needs of developers of all categories, Amazon has developed a comprehensive set of tools for scientists of different levels and project expertise. Among the testimonials, you can find praise from such renowned business as Vodafone, Expedia, Ryanair and many more. But apart from paid products, Amazon has AWS Free Tier and specifically its SageMaker for professionals that want to use a fully managed platform to create, train, and run machine learning models.

    Machine Learning Taste

    There are a variety of usage examples of the combination of ready-to-use machine learning APIs or custom machine learning software paired with different third parties tools or your internal data.

    To give you a brief understanding of how you can use such tools and improve your business with machine learning let imagine that you own a huge women apparel ecommerce website. You have a variety of different brands and models in stock right now. One of the biggest problems for every ecommerce company is the right stock management to minimize stock balance (reduction of storage costs and lost profits). Therefore, one of the main questions that confront each large online store is which brands and models and in what quantity to choose for the following seasons. The right choice may significantly lower stock balance and boost revenue!

    Let’s imagine that you want to take the whole power of machine learning and data science development and to understand which of the current brands and models your customers like, and which models will be popular in the next seasons. It is a quite likely situation, isn’t it?

    To achieve the best results for your business plan and stock planning you may gather/mine/extract the following data:

  • Get the whole sales history of all your goods from your CRM or stock management system. We assume that every item in your store has all details (types, categories, models, colors, features, etc.).
  • Download all comments of your customers related to specific items from your website.
  • Use several social media monitoring tools (like Hootsuite and/or Google Alerts) or directly to Twitter API for opinion mining and gather all mentions of every apparel brand and model.
  • Check RSS feeds of fashion magazines or some popular beauty bloggers and gather all articles related to some specific category or niche.
  • After you gather all this data and they would be prepared for further usage, you can apply some machine learning technics:

  • Use a data clustering technics to find out which types of clothes, features, colors, etc. are more popular and are sold better.
  • Check Google Cloud Translate to find out trends in different languages and in different countries (you can use this information later to make your offering targeting better if you operate worldwide).
  • Use Amazon Comprehend to check all mentions about every product, model, type of, let's say, a dress or its color and understand how positive or negative the text of the mention or review is.
  • Identify multiple entities in product reviews or social media mentions and link distinct entities between each other by associating text to additional information on the web with Azure Text Analytics.
  • Check the everything you’ve gathered from everything above mentioned to find out which products, types of clothes, colors, and etc. WILL BE more popular and satisfying among particular parts of your audience.
  • Profit!
  • Everything above mentioned is possible without data at all, and yes, you need a really skilled machine learning developer to achieve that (find out the data channels, pre-learing data preparations like data cleansing or building aggregate indicators, custom model training, overall system assembling). But we hope that now you understand that you don’t need an astonishing number of data to benefit from the power of ML.

    Whether you are new to the industry just making baby steps in machine learning or already felt a taste of ML magic that cut down costs, there are alternative sources and technics to power up your business and make the most of emerging technology to boost your revenue.

    This story is published in The Startup, Medium’s largest entrepreneurship publication followed by + 377,345 people. Subscribe to receive our top stories here.

    The Key Factor That Influences The Adoption Of Cloud-Based Machine Learning Platforms | killexams.com real questions and Pass4sure dumps

    Cloud computing has influenced the rise of machine learning and artificial intelligence. Factors such as affordable storage, availability of GPUs and FPGAs and advancements in deep learning made machine learning accessible and affordable to businesses.

    ML Toolinghttps://flic.kr/p/WLNjmS

    Mainstream cloud providers have shifted their focus from pushing traditional IaaS to selling PaaS based on machine learning. Cognitive APIs, automated ML, model management and preconfigured data science VMs backed by GPUs are going to drive the consumption of public cloud. AI and ML platforms are becoming the key differentiating factors for choosing a public cloud provider.

    One factor that has tremendous influence in driving the adoption of ML platforms in the cloud is developer tools. As developers start to collaborate with data scientists to build and deploy machine learning models, they look for familiar, productive environments. The tools available for data preparation, experimentation, model management and model serving are critical for the success of an ML platform in the cloud.

    Cloud providers are expected to build the tooling for both seasoned data scientists and traditional enterprise developers experienced in developing web and mobile applications. Given the difference in these personas, it is not an easy task for cloud vendors to build ML tools that appeal to developers as well as data scientists.

    Jupyter Notebooks are interactive, browser-based environments to build machine learning and predictive analytics applications. They provide a collaborative platform for both developers and data scientists to work on the same problem. Jupyter Notebooks can be used to develop Python and R programs that are typically used in building ML models. Native integration with Github and browser-based access have made them popular with developers and data scientists.

    Traditional integrated development environments such as Visual Studio and Eclipse are not ideal for ML development. They lack features for interactive visualizations and rapid iterations for experimenting with models. Mainstream vendors are choosing Jupyter Notebooks over traditional IDEs for interacting with their ML platforms. Cloud providers are exposing APIs that can be seamlessly integrated with native Python and R libraries.

    AWS was the first cloud provider to embrace Jupyter Notebooks for Amazon SageMaker – an ML PaaS environment built on top of Amazon EC2. Developers can provision separate environments for training and hosting the ML models. SageMaker exposes APIs for relevant AWS services such as S3 and Redshift. The service exploits GPU-based EC2 instances for training ML models.

    Google introduced Colaboratory, a notebook-based environment that runs within Google Drive. This service is backed by an NVIDIA Tesla GPU to accelerate model training. Interestingly, Google’s official ML PaaS – Cloud ML Engine – doesn’t include much of developer tooling. GCP customers are expected to use local environments based on Jupyter, PyCharm or Spyder before moving the models to the cloud.

    IBM’s Watson Studio is built on open source technologies to make it familiar to data scientists and developers. It integrates with Jupyter Notebooks, Anaconda and RStudio. Watson Studio is available within the browser and desktop interfacing with the public and private cloud. The tool offers integrated access to IBM Cloud pre-trained machine learning models such as Visual Recognition, Watson Natural Language Classifier and others.

    Microsoft experimented creating an exclusive tool for ML development that was closely tied to Azure. Branded as Azure ML Workbench, the IDE was neither based on Visual Studio nor Jupyter Notebooks. The tool that was in the preview for over a year didn't receive a favorable response from customers. Microsoft promptly killed ML Workbench and moved into Jupyter Notebook-based tooling. Recently at Microsoft Ignite, the company unveiled a new set of ML tools and APIs that simplify the process of building and deploying models. Azure ML Service, the latest ML-based PaaS from Microsoft, takes a leaf from Amazon SageMaker. It relies heavily on Jupyter Notebooks, Python APIs, and containers for managing the lifecycle of ML models. Azure ML Studio offers an alternative, browser-based tool to build and deploy ML models interactively.

    Developer tools are fast becoming the key to driving the adoption of ML platforms. Cloud providers should ensure that they have accessible, familiar and collaborative tools that appeal to data scientists and developers.



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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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