New Azure services help more people realize the possibilities of big data

By T. K. “Ranga” Rengarajan as written on blogs.microsoft.com
This week in San Jose thousands of people are at Strata + Hadoop World to explore the technology and business of big data. As part of our participation in the conference, we are pleased to announce new and enhanced Microsoft data services: a preview of Azure HDInsight running on Linux, the general availability of Storm on HDInsight, the general availability of Azure Machine Learning, and the availability of Informatica technology on Azure.
These new services are part of our continued investment in a broad portfolio of solutions to unlock insights from data. They can help businesses dramatically improve their performance, enable governments to better serve their citizenry, or accelerate new advancements in science. Our goal is to make big data technology simpler and more accessible to the greatest number of people possible: big data pros, data scientists and app developers, but also everyday businesspeople and IT managers. Azure is at the center of our strategy, offering customers scale, simplicity and great economics. And we’re embracing open technologies, so people can use the tools, languages and platforms of their choice to pull the maximum value from their data.
Simply put, we want to bring big data to the mainstream.
Azure HDInsight, our Apache Hadoop-based service in the cloud, is a prime example. It makes it easy for customers to crunch petabytes of all types of data with fast, cost-effective scale on demand, as well as programming extensions so developers can use their favorite languages. Customers like Virginia Tech, Chr. Hanson, Mediatonic and many others are using it to find important data insights. And, today, we are announcing that customers can run HDInsight on Ubuntu clusters (the leading scale-out Linux), in addition to Windows, with simple deployment, a managed service level agreement and full technical support. This is particularly compelling for people that already use Hadoop on Linux on-premises like on Hortonworks Data Platform, because they can use common Linux tools, documentation, and templates and extend their deployment to Azure with hybrid cloud connections.











Storm for Azure HDInsight, generally available today, is another example of making big data simpler and more accessible. Storm is an open source stream analytics platform that can process millions of data “events” in real time as they are generated by sensors and devices. Using Storm with HDInsight, customers can deploy and manage applications for real-time analytics and Internet-of-Things scenarios in a few minutes with just a few clicks. Linkury is using HDInsight with Storm for its online monetization services, for example. We are also making Storm available for both .NET and Java and the ability to develop, deploy, and debug real-time Storm applications directly in Visual Studio. That helps developers to be productive in the environments they know best.
You can read this blog to learn about these and other updates we’re making to HDInsight to make Hadoop simpler and easier to use on Azure.
Azure Machine Learning, also generally available today, further demonstrates our commitment to help more people and organizations use the cloud to unlock the possibilities of data. It is a first-of-its-kind, managed cloud service for advanced analytics that makes it dramatically simpler for businesses to predict future trends with data. In mere hours, developers and data scientists can build and deploy apps to improve customer experiences, predict and prevent system failures, enhance operational efficiencies, uncover new technical insights, or a universe of other benefits. Such advanced analytics normally take weeks or months and require extensive investment in people, hardware and software to manage big data. Also, now developers – even those without data science training – can use the Machine Learning Marketplace to find APIs and finished services, such as recommendations, anomaly detection and forecasting, in order to deploy solutions quickly. Already customers like Pier 1, Carnegie Mellon, eSmart Systems, Mendeley and ThyssenKrupp are finding value in their data with Azure Machine Learning.

Azure Machine Learning reflects our support for open source. The Python programming language is a first class citizen in Azure Machine Learning Studio, along with R, the popular language of statisticians. New breakthrough algorithms, such as “Learning with Counts,” now allow customers to learn from terabytes of data. A new community gallery allows data scientists to share experiments via Twitter and LinkedIn, too. You can read more about these innovations and how customers are using Azure Machine Learning in this blog post.
Another key part of our strategy is to offer customers a wide range of partner solutions that build on and extend the benefits of Azure data services. Today, data integration leader Informatica is joining the growing ecosystem of partners in the Azure Marketplace. The Informatica Cloud agent is now available in Linux and Windows virtual machines on Azure. That will enable enterprise customers to create data pipelines from both on-premises systems and the cloud to Azure data services such as Azure HDInsight, Azure Machine Learning, Azure Data Factory and others, for management and analysis.
The value provided by our data services multiplies when customers use them together. A case in point is Ziosk, maker of the world’s first ordering, entertainment and pay-at-the table tablet. They are using Azure HDInsight, Azure Machine Learning, our Power BI analytics service and other Microsoft technologies to help restaurant chains like Chili’s drive guest satisfaction, frequency and advocacy with data from tabletop devices in 1,400 locations.
This week the big data world is focused on Strata + Hadoop World, a great event for the industry and community. It’s exciting to consider the new ideas and innovations happening around the world every day with data. Here at Microsoft, we’re thrilled to be part of it and to fuel that innovation with data solutions that give customers simple but powerful capabilities, using their choice of tools and platforms in the cloud.


What is Azure Machine Learning Studio?

Microsoft Azure Machine Learning Studio is a collaborative visual development environment that enables you to build, test, and deploy predictive analytics solutions that operate on your data. The Machine Learning service and development environment is cloud-based, provides compute resource and memory flexibility, and eliminates setup and installation concerns because you work through your web browser.
Machine Learning Studio is where data science, predictive analytics, cloud resources, and your data meet.

The Machine Learning Studio interactive workspace

To develop a predictive analysis model, you typically use data from one or more sources, transform and analyze that data through various data manipulation and statistical functions, and generate a set of results. Developing a model like this is an iterative process. As you modify the various functions and their parameters, your results converge until you are satisfied that you have a trained, effective model.
Machine Learning Studio gives you an interactive, visual workspace to easily build, test, and iterate on a predictive analysis model. You drag-and-drop datasets and analysis modules onto an interactive canvas, connecting them together to form an experiment, which you run in Machine Learning Studio. To iterate on your model design, you edit the experiment, save a copy if desired, and run it again. When you're ready, you can publish your experiment as a web service so that your model can be accessed by others.
There is no programming required, just visually connecting datasets and modules to construct your predictive analysis model.
To learn how Azure and/or cloud computing can revolutionize your business, fill out the contact form to the right or call us at 800-550-3795.
Try Azure Machine Learning for free. No credit card or Azure subscription needed. Get started now

Source: https://azure.microsoft.com/en-us/documentation/articles/machine-learning-what-is-ml-studio/

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