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…

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…

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…

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

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

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