Tags : #AzureML

20

Sep

Invoking Azure ML Web Services from Excel, Using Power Query
Invoking Azure ML Web Services from Excel, Using Power Query

This post is authored by Amita Gajewar, Senior Data Scientist at Microsoft.

Problem Statement

Microsoft Azure Machine Learning Studio lets data scientists build machine learning models for a variety of problems that require predictive analytics capabilities. The Studio publishes these models as web services which can then be invoked via REST APIs, i.e. to send it data and get back predictions.

For business analysts who spend a lot of time manipulating and visualizing data in spreadsheets, it would very useful to be able to invoke an Azure ML web service from right within that environment, by passing in appropriate parameters and have the results populated back into the spreadsheet. To go one step further, they would be able to process and format the returned results, before displaying them in tables or charts.

In this post, we explain how to accomplish this using Power Query and macros within Excel.

Power Query provides a method to query, combine and refine data across a wide variety of sources including databases, the web, Hadoop and more. For illustration purposes, I create a Power Query to invoke an Azure ML web service that forecasts various financial metrics (e.g., revenue, EPS, etc.) for the 30 Dow

12

Sep

Free edX Course – Introduction to Artificial Intelligence (AI)
Free edX Course – Introduction to Artificial Intelligence (AI)

Wondering what Artificial Intelligence, or AI, is all about? Where does data science leave off? And where and how does machine learning apply?

AI will likely define the next generation of software. Given all the talk and confusing terminology out there, we’ve got the perfect overview course for those of you who are just getting started. See what’s really going on with AI and explore the concepts and technologies behind it. Join expert Graeme Malcolm, and spend 3-4 hours each week over four weeks to start using AI to build your own intelligent apps.

There are no programming prerequisites for this course, but Python experience might help, as would an understanding of variables, loops, branching, and other software basics.

Discover how machine learning can be used to build predictive models for AI. Learn how software can be used to process, analyze, and extract meaning from natural language and to process images and video to understand the world the way we do. Find out how to build intelligent bots that enable conversational communication between humans and AI systems.

This course will help you learn how to:

Build simple machine learning models with Azure Machine Learning.
Use Python and Microsoft

07

Sep

A Digital Health Companion, Virtual Banking Assistant & Smart Water Monitoring System – All Built on Microsoft AI

A selective peek at new customers benefiting from Microsoft AI & Machine Learning.

Ruppiner Kliniken Creates a Digital Companion That Can Save Lives

17 million people die of cardiovascular disease each year. Many of these victims could survive if their physicians became aware of critical changes occurring in their vascular system and heart in a timely manner. Unfortunately, in many such cases, there are no external symptoms to report. In other cases, patients fail to report symptoms to their physician until it’s too late.

The cardiology department at Ruppiner Kliniken, a leading hospital in Germany, was motivated to save the lives of as many such patients as they could. They realized that they needed a way to collect patients’ data health in an unobtrusive way, without interrupting their daily lives. When fitness bands hit the mainstream market a few years ago, they realized their potential in improving patient care. By enhancing the fitness band with new types of tiny wireless sensors that collect ECG, blood pressure, and a variety of other medical readings, it would be possible to collected a patient’s data over weeks, months, or even longer, and consolidate and analyze such data to get a much more accurate

06

Sep

Free Microsoft Webinar on the Interactive Price Analytics Solution, Now Available in Azure

Join us for a free Microsoft webinar on Interactive Price Analytics and learn more about this fascinating topic. At the end of it, you will be able to use our new solution right out-of-the-box, or work with a Microsoft solution partner to you customize the solution further, to meet your business specific needs.

The webinar runs from 10-11 AM Pacific next Tuesday, September 12th, and will be presented by Tomas Singliar, Senior Data Scientist at Microsoft.

Every business needs data-driven insights around product pricing. However, getting to the bottom of the true effects of pricing can be quite tricky. For instance, how much more of that turkey did you end up selling because it was steeply discounted versus how much of it was because it was right before Thanksgiving?

First off, you need a team of data scientists. They will tell you to run many experiments, but you’d need engineers to build a system for that. There are also many constraints. For instance, in the world of physical retail, you can often only change prices on a limited number of products at a time, and that too, perhaps only a few times each month, as new price stickers need to

06

Sep

How to Train & Serve Deep Learning Models at Scale, Using Cognitive Toolkit with Kubernetes on Azure

This post is authored by Wee Hyong Tok, Principal Data Science Manager at Microsoft.

Deep Learning has fueled the emergence of many practical applications and experiences. It has played a central role in making many recent breakthroughs possible, ranging from speech recognition that’s reached human parity in word recognition during conversations, to neural networks that are accelerating the creation of highly precise land cover datasets, to predicting vision impairment, regression rates and eye diseases, among others.

The Microsoft Cognitive Toolkit (CNTK) is the behind-the-scenes magic that makes it possible to train deep neural networks that address a very diverse set of needs, such as in the scenarios above. CNTK lets anyone develop and train their deep learning model at massive scale. Successful deep learning projects also require a few critical infrastructure ingredients – specifically, an infrastructure that:

Enables teams to perform rapid experimentation.
Scales, based on the demands of training.
Handles the increasing load needed to serve a trained model.

Meanwhile, container technologies have been maturing, with more enterprises using containers in their IT environments. Containers are allowing organizations to simplify the development and deployment of applications in various environments (e.g. on-premises, public cloud, hybrid cloud, etc.). Various container orchestration

29

Mar

Microsoft Makes Big Data and Analytics Easier in the Cloud

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft.

This week I’m joining thousands of people attending Strata + Hadoop World in San Jose to explore the technology and business of big data and data science. As part of our participation in the conference, we are announcing several important investments to continue delivering on our commitment to make big data processing and analytics simpler and more accessible:

Advanced analytics at scale with R Server for HDInsight and the latest version of Spark for HDInsight are now available in preview: Customers can leverage their existing R skills and reuse current code to run at scale. R Server for HDInsight offers popular scalable R algorithms and the ability to parallelize any existing R function. We are also releasing the latest version of Spark for HDInsight, which can deliver 7x performance over MapReduce for most analytics. These capabilities give our customers the ability to train and run advanced analytics and ML models on larger datasets, and much faster than previously possible in the cloud. Out-of-the-box application integration, providing easier access to popular big data apps: Customers can now discover and deploy popular big data applications with HDInsight…

29

Mar

Microsoft Makes Big Data and Analytics Easier in the Cloud

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft.

This week I’m joining thousands of people attending Strata + Hadoop World in San Jose to explore the technology and business of big data and data science. As part of our participation in the conference, we are announcing several important investments to continue delivering on our commitment to make big data processing and analytics simpler and more accessible:

Advanced analytics at scale with R Server for HDInsight and the latest version of Spark for HDInsight are now available in preview: Customers can leverage their existing R skills and reuse current code to run at scale. R Server for HDInsight offers popular scalable R algorithms and the ability to parallelize any existing R function. We are also releasing the latest version of Spark for HDInsight, which can deliver 7x performance over MapReduce for most analytics. These capabilities give our customers the ability to train and run advanced analytics and ML models on larger datasets, and much faster than previously possible in the cloud. Out-of-the-box application integration, providing easier access to popular big data apps: Customers can now discover and deploy popular big data applications with HDInsight…

29

Mar

Microsoft Makes Big Data and Analytics Easier in the Cloud

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft.

This week I’m joining thousands of people attending Strata + Hadoop World in San Jose to explore the technology and business of big data and data science. As part of our participation in the conference, we are announcing several important investments to continue delivering on our commitment to make big data processing and analytics simpler and more accessible:

Advanced analytics at scale with R Server for HDInsight and the latest version of Spark for HDInsight are now available in preview: Customers can leverage their existing R skills and reuse current code to run at scale. R Server for HDInsight offers popular scalable R algorithms and the ability to parallelize any existing R function. We are also releasing the latest version of Spark for HDInsight, which can deliver 7x performance over MapReduce for most analytics. These capabilities give our customers the ability to train and run advanced analytics and ML models on larger datasets, and much faster than previously possible in the cloud. Out-of-the-box application integration, providing easier access to popular big data apps: Customers can now discover and deploy popular big data applications with HDInsight…

21

Mar

Hadoop is famously scalable. Cloud computing is famously scalable. But R – the preferred software and lingua franca of data scientists worldwide – not so much. But what if we seamlessly combined Hadoop with the cloud and R to create a scalable data science platform? Imagine exploring, transforming, modeling, and scoring data at any scale from the comfort of your favorite R environment. Now, imagine calling a simple R function to operationalize your predictive model as a scalable, cloud-based web service. 

Learn how to leverage the magic of Hadoop on-premises or in the cloud to run your R code, with thousands of open source R extension packages, and distributed implementations of the most popular machine learning algorithms, at scale. Click here or on the image below to register for this free webinar.

ML Blog Team

21

Mar

Hadoop is famously scalable. Cloud computing is famously scalable. But R – the preferred software and lingua franca of data scientists worldwide – not so much. But what if we seamlessly combined Hadoop with the cloud and R to create a scalable data science platform? Imagine exploring, transforming, modeling, and scoring data at any scale from the comfort of your favorite R environment. Now, imagine calling a simple R function to operationalize your predictive model as a scalable, cloud-based web service. 

Learn how to leverage the magic of Hadoop on-premises or in the cloud to run your R code, with thousands of open source R extension packages, and distributed implementations of the most popular machine learning algorithms, at scale. Click here or on the image below to register for this free webinar.

ML Blog Team