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 a rich collection of tools, seen in the picture below, and makes you more productive when it comes to building and deploying advanced machine learning and analytics apps. The central theme of our latest Linux DSVM release is to enable the development and testing of ML apps for deployment to distributed scalable platforms such as Spark, Hadoop and Microsoft R Server, for operating on data at a very large scale. In addition, with this release, DSVM also offers Julia Computing's JuliaPro on both Linux and Windows editions.

The Linux Data Science Virtual machine (DSVM) is a custom Azure VM built on OpenLogic CentOS 7.2 based Linux with several popular tools for data science modeling/development like: * Microsoft R Server Developer Edition * Anaconda Python distribution, * JupyterHub - a multiuser Jupyter notebooks server supporting Python and R * Postgres database * Eclipse, Azure tools, libraries to access various Azure services like AzureML, databases, big data services. Here is a list of key software on the Data Science Virtual Machine and comparison between the Windows and Linux editions of the product.