The Microsoft Data Science Virtual Machine is an Azure virtual machine (VM) image pre-installed and configured with several popular tools that are commonly used for data analytics and machine learning. Some of the tools included are Microsoft R Server Developer Edition, Anaconda Python distribution, Azure SDK and more Microsoft today announced the availability of the Linux Data Science Virtual Machine on the Azure marketplace. This custom VM image built is on the OpenLogic CentOS-based Linux version 7.2. Find the list of tools that are pre-installed and pre-configured on the Linux Data Science Virtual Machine below,
- Microsoft R Open (with Intel Math Kernel Library).
- Anaconda Python Distribution with Python 2.7 and 3.5.
- Jupyter Notebooks with Python and R kernel for browser based data exploration and development.
- Azure tools: Azure Command Line Interface for managing Azure resources, Azure Storage Explorer for working with Azure Blobs.
- A local Postgres database instance.
- Machine Learning Tools:
- Azure ML: Productionize R and Python models built locally on the VM to our cloud based Azure ML service through pre-installed libraries.
- Computational Network Toolkit (CNTK): A deep learning software from Microsoft Research.
- Vowpal Wabbit: An ML system supporting techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
- XGBoost: A tool providing fast and accurate boosted tree implementation.
- Rattle (the R Analytical Tool To Learn Easily): A GUI tool that makes it very easy to get started with data analytics in R, with graphical data exploration, ML models and R code generation.
- Development Tools: Azure SDK in Java, Python, Node.js, Ruby, PHP; Eclipse IDE with Azure Toolkit plugin; code editors like vim, gedit and Emacs (with ESS, auctex add-ons); SQL Server drivers and command line tools like bcp (Bulk Copy), sqlcmd (text based SQL Server query utility); SQuirreL SQL graphical client to access various databases.
- Remote access on textual interface through an SSH client (like PuTTY or ssh command) or on a graphical desktop (needs separate one-time install of X2Go on your client machine).
Read more about it here.