Category Archives : #AzureML

26

Oct

Anaconda and Microsoft Partner to Offer Python and R for Powerful Machine Learning

This post was authored by Nagesh Pabbisetty, Partner Director of Program Management, Microsoft Machine Learning Services.

Recently, at Strata Data Conference in New York City, Microsoft and Anaconda announced an exciting partnership to make Anaconda Python distribution into SQL Server, Machine Learning Server, Azure Machine Learning, and Visual Studio to deliver real-time insights. In addition, Anaconda will be distributing Microsoft R. Let’s take a deeper look at this exciting new partnership.

Microsoft is committed to helping developers build AI powered applications by enabling them to do machine learning and AI wherever their data is. SQL Server 2017 includes Machine Learning Services — enterprise grade in-database machine learning capabilities with R and Python languages. Machine Learning Server enables customers to do scalable machine learning using R or Python on standalone Windows and Linux servers, Hadoop clusters and Azure data platforms.

Anaconda is the leading distribution of Python leveraged by millions of users today. A strong partnership with this popular Python distribution for data science further strengthens Microsoft’s goal of building tools to empower every organization to build their own AI capabilities.

Microsoft and Anaconda built a customized Anaconda distribution – Anaconda for Microsoft for doing machine learning with Microsoft products and

25

Oct

AI for Education: Individualized Code Feedback for Thousands of Students

This post is authored by Matthew Calder, Senior Business Strategy Manager, and Ke Wang, Research Intern at Microsoft.

There are more than 9,000 students enrolled in the Microsoft Introduction to C# course on edX.org. Although course staff can’t offer the type of guidance available in an on-campus classroom setting, students can receive personalized help, thanks to a project from Microsoft Research. When a student’s assignment contains mistakes, that student—within seconds—receives a message specific to their code submission. Beyond just informing the student that their program doesn’t work, Microsoft has created a tool which automatically generates feedback that precisely identifies errors and even hints at how to correct them. Students are happy to get fast, focused guidance so they can concentrate on learning new skills rather than on just troubleshooting.

Learning from Thousands of Students

Ke Wang is the Microsoft Research Intern who has led this project since summer 2016. Paul Pardi, Principal Content Publishing Manager in Microsoft Learning, initiated the project because his team was looking to better teach the thousands of students who take their Massive Open Online Courses (MOOCs) through a partnership with edX.org. The idea was to see more students completing their courses with the help of

25

Oct

AI for Education: Individualized Code Feedback for Thousands of Students

This post is authored by Matthew Calder, Senior Business Strategy Manager, and Ke Wang, Research Intern at Microsoft.

There are more than 9,000 students enrolled in the Microsoft Introduction to C# course on edX.org. Although course staff can’t offer the type of guidance available in an on-campus classroom setting, students can receive personalized help, thanks to a project from Microsoft Research. When a student’s assignment contains mistakes, that student—within seconds—receives a message specific to their code submission. Beyond just informing the student that their program doesn’t work, Microsoft has created a tool which automatically generates feedback that precisely identifies errors and even hints at how to correct them. Students are happy to get fast, focused guidance so they can concentrate on learning new skills rather than on just troubleshooting.

Learning from Thousands of Students

Ke Wang is the Microsoft Research Intern who has led this project since summer 2016. Paul Pardi, Principal Content Publishing Manager in Microsoft Learning, initiated the project because his team was looking to better teach the thousands of students who take their Massive Open Online Courses (MOOCs) through a partnership with edX.org. The idea was to see more students completing their courses with the help of

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

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