Category Archives : #AzureML

20

Oct

Free Webinars on Cognitive Toolkit with Batch AI, DSVM & Document Collection Analysis

Join us at a set of three exciting webinars starting on Tuesday next week where we’ll show you how to train distributed convolution neural networks using Microsoft Cognitive Toolkit (aka CNTK) and Batch AI, how to do AI development using the latest version of the Data Science Virtual Machine (DSVM), and how to use Document Collection Analysis to gain insights from large sets of documents and serve your downstream NLP tasks. All sessions are entirely free, of course. More on each session below – be sure to click on the links attached to the titles of these sessions to reserve your spot now.

Train a Distributed Convolutional Neural Network using Microsoft Cognitive Toolkit and Batch AI

Deep Learning has become the de facto standard for most computer vision tasks since its breakthrough year in 2012 at the ImageNet Challenge. In the past few years, taking advantage of more complex and deeper neural network architectures, deep learning algorithms have met and exceeded human-level performance in image recognition. Increasingly, computer vision applications are starting to apply deep learning technologies, with plenty of them seeing tremendous success. Nevertheless, training deep learning networks on a large data set remains quite challenging. The sheer amount

19

Oct

Announcing the Data Science Virtual Machine in Batch AI Service
Announcing the Data Science Virtual Machine in Batch AI Service

This post is authored by Paul Shealy, Senior Software Engineer at Microsoft.

We are pleased to announce the integration of the Microsoft Data Science Virtual Machine (DSVM) with the Batch AI service in Azure.

DSVM is a family of popular VM images published on Azure with a broad choice of machine learning, AI and data science tools. All tools are pre-configured giving you a ready-to-use, on-demand, elastic environment in the cloud to help you perform data analytics and AI development productively. You focus less on IT administrative tasks and more on your data science with the DSVM.

Microsoft’s Batch AI Service is a new service that helps you train and test machine learning models, including deep learning models, on pools of GPU machines. It simplifies the process of creating a cluster of machines and training on it using many popular deep learning frameworks like TensorFlow, Microsoft Cognitive Toolkit, and others. Batch AI also lets you run parameter sweeps in parallel. Managing data is an integral part of deep learning, and Batch AI includes native support for file shares and NFS servers.

The Ubuntu DSVM is supported as a native VM image in Batch AI. The Ubuntu DSVM comes

09

Oct

The Microsoft Team Data Science Process (TDSP) – Recent Updates

This post is authored by Xibin Gao, Data Scientist, Wei Guo, Data Scientist, Brad Severtson, Senior Content Developer, and Debraj GuhaThakurta, Senior Data Scientist Lead, at Microsoft

What is TDSP

Improving the efficiency of developing and deploying data science solutions requires an efficient process to complement the data platforms and data science tools that you use. Many enterprise data science teams today face challenges pertaining to standardization, collaboration and the use of appropriate DevOps practices when developing and deploying their advanced analytics solutions.

We developed and released the Team Data Science Process (TDSP), an open GitHub project, to address these very challenges. TDSP is currently helping numerous data science teams in Microsoft and at other organizations to standardize their data science projects, adopt collaborative development and DevOps practices. TDSP was first released at Ignite in September 2016.

In this blog post, we provide an overview of recent developments involving TDSP, including recent releases and how its adoption has gone since our first public release.

Recent Releases

Since our 2016 launch, we’ve made the following updates to TDSP:

Standardized Data Exploration and Reporting: IDEAR in Microsoft R Server (MRS) and Python

Standardization of data science projects and their artifacts is

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