Take Advantage of Scalable Cloud Compute Directly from Your R Session, with doAzureParallel

Re-posted from the Azure blog. For users of the R language, scaling up their work to take advantage of cloud compute has generally been a complex undertaking. We are therefore excited to announce doAzureParallel, a lightweight R package built on Azure Batch that allows you to easily use Azure’s flexible compute resources right from your… Read more

Build & Deploy Machine Learning Apps on Big Data Platforms with Microsoft Linux Data Science Virtual Machine

This post is authored by Gopi Kumar, Principal Program Manager in the Data Group at Microsoft. This post covers our latest additions to the Microsoft Linux Data Science Virtual Machine (DSVM), a custom VM image on Azure, purpose-built for data science, deep learning and analytics. Offered in both Microsoft Windows and Linux editions, DSVM includes… Read more

Cloud-Scale Text Classification with Convolutional Neural Networks on Microsoft Azure

This post is by Miguel Fierro, Ilia Karmanov, Thomas Delteil, Andreas Argyriou, and Max Kaznady, all Data Scientists at Microsoft. Natural Language Processing (NLP) is one of the fields in which deep learning has made significant progress. Specifically, the area of text classification, where the objective is to categorize documents, paragraphs or individual sentences into… Read more

Julia – A Fresh Approach to Numerical Computing

This post is authored by Viral B. Shah, co-creator of the Julia language and co-founder and CEO at Julia Computing, and Avik Sengupta, head of engineering at Julia Computing. The Julia language provides a fresh new approach to numerical computing, where there is no longer a compromise between performance and productivity. A high-level language that… Read more

New Year & New Updates to the Windows Data Science Virtual Machine

This post is authored by Gopi Kumar, Principal Program Manager in the Data Group at Microsoft. First of all, a big thank you to all users of the Data Science Virtual Machine (DSVM) for your tremendous response to our offering in 2016. We’re looking forward to a similarly great year in 2017. The new year… Read more

Announcing Data Science Utilities Version 0.11, for the Team Data Science Process

This post is authored by Hang Zhang, Senior Data Scientist Manager, Gopi Kumar, Principal Program Manager, and Xibin Gao, Data Scientist, at Microsoft. Back in September 2016, we released an early public preview of Team Data Science Process (TDSP), with the goal of supporting secure collaboration within enterprise data science organizations, with capabilities such as… Read more

Hello 2017, and Recap of Top 10 Posts of 2016

As we kick off what will surely be another very exciting year of progress in artificial intelligence, machine learning and data science, we start with a quick recap of our “Top 10” most popular posts (based on aggregate readership) from the year just concluded. Here are the posts that had the most page views in… Read more

Exploring Azure Data with Apache Drill, Now Pre-Installed on the Microsoft Data Science Virtual Machine

This post is authored by Gopi Kumar, Principal Program Manager in Microsoft’s Data Group. We recently came across Apache Drill, a very interesting data analytics tool. The introduction page to Drill describes it well: “Drill is an Apache open-source SQL query engine for Big Data exploration. Drill is designed from the ground up to support… Read more

Using SQL Server 2016 with R Services for Campaign Optimization

This post is authored by Nagesh Pabbisetty, Partner Director of Program Management at Microsoft. We are happy to announce a new Campaign Optimization solution based on R Services in SQL Server 2016, designed to help customers apply machine learning to increase response rates from their leads. This post contains more information about this new solution…. Read more

Deep Learning Made Easy in Azure

This post was authored by Anusua Trivedi, Data Scientist, Microsoft and Jamie Olson, Analytics Solution Architect, Microsoft Deep learning is an exciting new space for predictive modeling and machine learning and I’ve previously written about a variety of different models and tools in my previous blogs.  However, it can be intimidating to get started and… Read more