The Microsoft Data Science Virtual Machine (DSVM) is a powerful data science development environment that enables you to perform various data exploration and modeling tasks. The environment comes already built and bundled with several popular data analytics tools that make it easy to get started quickly with your analysis for On-premises, Cloud or hybrid deployments. The DSVM works closely with many Azure services and is able to read and process data that is already stored on Azure, in Azure SQL Data Warehouse, Azure Data Lake, Azure Storage, or in Azure Cosmos DB. It can also leverage other analytics tools such as Azure Machine Learning and Azure Data Factory.
The ‘Data Science Virtual Machine (DSVM)’ is a ‘Windows Server 2016 with Containers’ VM & includes popular tools for data exploration, analysis, modeling & development.
- Microsoft R Server – Dev. Ed. (Scalable R)
- Anaconda Python
- SQL Server 2016 Dev. Ed. – With In-Database R analytics
- Microsoft Office 365 ProPlus BYOL – Shared Computer Activation
- Julia Pro + Juno Editor
- Jupyter notebooks
- Visual Studio Community Ed. + Python, R & node.js tools
- Power BI Desktop
- Deep learning tools e.g. Microsoft Cognitive Toolkit (CNTK 2.0), TensorFlow & mxnet
- ML algorithm libraries e.g. xgboost, Vowpal Wabbit
- Azure SDKs + libraries for various Azure Cloud offerings. Integration tools are included for:
Tools for ML model operationalization as web services in the cloud, using Azure ML or Microsoft R Server.
Creating the Azure DSVM with Windows Server 2016
I use this Azure DSVM for testing and I will enable Auto-Shutdown Scheduler
(to save money)
The following software is by default installed in the Azure DSVM :
My Azure Data Science Virtual Machine in the Cloud 😉
From here you can configure your Data Management Gateway.
Microsoft Data Management Gateway connects on-premises data sources to cloud services for consumption. With Microsoft cloud services, such as Power BI for Office 365 and Azure Data Factory you get benefits including fast deployment, low maintenance cost, and flexible billing model while keeping your enterprise data on-premises. With Data Management Gateway, you can connect on-premises data to cloud services in a secure and managed way, to respond more quickly to changing business needs with a flexible, hybrid cloud platform. You can benefit from Microsoft cloud services while you keep your business running with the on-premises data.
More information about the Data Management Gateway :
Running Azure Data Science VM
Portal Overview of the Azure DSVM
From the Microsoft Azure Portal you can add Resource Extensions to your Azure Data Science VM.
From here you can add the following Extensions :
Here you can find more Technical information about Microsoft Azure Data Science Virtual Machine :