Quickstart: Set up the Data Science Virtual Machine for Windows

Get up and running with a Windows Data Science Virtual Machine.

Prerequisite

To create a Windows Data Science Virtual Machine, you must have an Azure subscription. Try Azure for free.

Create your DSVM

To create a DSVM instance:

  1. Go to the Azure portal You might be prompted to sign in to your Azure account if you're not already signed in.

  2. Find the virtual machine listing by typing in "data science virtual machine" and selecting "Data Science Virtual Machine - Windows 2016." Windows VM Listing

  3. Select the Create button at the bottom.

    Virtual machine listing on the Azure portal, with Create button

  4. You should be redirected to the "Create a virtual machine" blade. Basics tab corresponding to Windows Virtual Machine

  5. Fill in the Basics tab:

    • Subscription: If you have more than one subscription, select the one on which the machine will be created and billed. You must have resource creation privileges for this subscription.
    • Resource group: Create a new group or use an existing one.
    • Virtual machine name: Enter the name of the virtual machine. This is how it will appear in your Azure portal.
    • Location: Select the datacenter that's most appropriate. For fastest network access, it's the datacenter that has most of your data or is closest to your physical location. Learn more about Azure Regions.
    • Image: Leave the default value.
    • Size: This should auto-populate with a size that is appropriate for general workloads. Read more about Windows VM sizes in Azure.
    • Username: Enter the administrator username. This is the username you will use to log into your virtual machine, and need not be the same as your Azure username.
    • Password: Enter the password you will use to log into your virtual machine.
  6. Select Review + create.

  7. Review+create

    • Verify that all the information you entered is correct.
    • Select Create.

Note

  • You do not pay licensing fees for the software that comes pre-loaded on the virtual machine. You do pay the compute cost for the server size that you chose in the Size step.
  • Provisioning takes 10 to 20 minutes. You can view the status of your VM on the Azure portal.

Access the DSVM

After the VM is created and provisioned, follow the steps listed to connect to your Azure-based virtual machine. Use the admin account credentials that you configured in the Basics step of creating a virtual machine.

You're ready to start using the tools that are installed and configured on the VM. Many of the tools can be accessed through Start menu tiles and desktop icons.

You can also attach a DSVM to Azure Notebooks to run Jupyter notebooks on the VM and bypass the limitations of the free service tier. For more information, see Manage and configure Notebooks projects.

Next steps

  • Explore the tools on the DSVM by opening the Start menu.
  • Learn about the Azure Machine Learning service by reading What is Azure Machine Learning service? and trying out tutorials.
  • In File Explorer, browse to C:\Program Files\Microsoft\ML Server\R_SERVER\library\RevoScaleR\demoScripts for samples that use the RevoScaleR library in R that supports data analytics at enterprise scale.
  • Read the article Ten things you can do on the Data Science Virtual Machine.
  • Learn how to build end-to-end analytical solutions systematically by using the Team Data Science Process.
  • Visit the Azure AI Gallery for machine learning and data analytics samples that use Azure Machine Learning and related data services on Azure. We've also provided an icon for this gallery on the Start menu and on the desktop of the virtual machine.
  • Consult the appropriate reference documentation for this virtual machine.