title: "Getting Started | Microsoft Docs"

ms.date: "07/31/2017"

Jump-start your entry into machine learning by getting this complete machine learning solution from the Azure Marketplace. The data science virtual machine (frequently shortened to "DSVM") includes SQL Server, Microsoft Machine Learnign Server, and all development tools.

+ [Use R in SQL Server](/tutorials/sql-server-r-tutorials.md)

+ [Use Python in SQL Server](/tutorials/sql-server-r-tutorials.md)

ms.date: "07/26/2017"

However, Microsoft R Server provides a different mechanism for deployment support, using web services that support publishing of R jobs, and an administrative utility for running distributed R jobs. Microsoft R Server uses the functions in the **mrsdeploy** package to establish a session with remote compute nodes and execute R code in a console application.

Determine who can publish, update, and delete their own web services, those who can also update and delete the web services published by other users, and who can only list and consume web services. Learn more about [roles](https://msdn.microsoft.com/microsoft-r/operationalize/security-roles.md).

Typically, we do not recommend that you install Machine Learning Server on the same computer that is running SQL Server Machine Learning Services. We recommend that you install **Microsoft Machine Learning Server** on a separate computer from SQL Server, and then configure the operationalization features on that computer.

4. When done, open the [Administrator Utility](https://docs.microsoft.com/r-server/operationalize/configure-use-admin-utility).

ms.date: "07/19/2017"

+ Supports multiple compute contexts, remote or local

MicrosoftML packages are also proided for both R and Python. For more information, see [Using MicrosoftML in SQL Server](../using-the-microsoftml-package.md)

+ Use [RxOdbcData](https://docs.microsoft.com/r-server/python-reference/revoscalepy/rxodbbcdata) to create a data source from an ODBC connection

|`rx_oneclass_svm` | Creates a SVM model for imabalnced datsets, for use in anomaly detection|

ms.date: "07/14/2017"

For a simple example, see [this tutorial](/tutorials/rtsql-create-a-predictive-model-r.md)

ms.date: "07/31/2017"

This topic describes the package management features that you can use to manage R packages that are running on an instance of SQL Server.

- **rpkgs-users** Allows users to use any shared packages that were installed by members of the **rpkgs-shared** role.

- **rpkgs-private** Provides access to shared packages with the same permissions as the **rpkgs-users** role. Members of this role can also install, remove and use privately scoped packages.

- **rpkgs-shared** Provides the same permissions as the **rpkgs-private** role. Users who are members of this role can also install or remove shared packages.

- **db_owner** - Has the same permissions as the **rpkgs-shared** role. Can also grant users the right to install or remove both shared and private packages.

The following package management functions are provided in RevoScaleR, for installation and removal of packages in a specified compute context.

These packages are also included by default in SQL Server 2017. You can add the packages to an instance of SQL Server 2016 if you upgrade the instance to use at least Microsoft R 9.0.1. For more information, see [Using SqlBindR.exe to Upgrade R](/use-sqlbindr-exe-to-upgrade-an-instance-of-sql-server.md).

*Shared scope* means that users who have been given permission to the shared scope role (**rpkgs-shared**) can install and uninstall packages to a specified database. A package that is installed in a shared scope library can be used by other users of the database on SQL Server, provided those users are allowed to use installed R packages.

*Private scope* means that users who have been given membership in the private scope role (**rpkgs-private**) can install or uninstall packages into a private library location defined per user. Therefore, any packages installed in the private scope can be used only by the user who installed them. In other words, a user on SQL Server cannot use private packages that were installed by a different user.

Another scenario might require greater isolation among users, or use of different versions of packages. In that case, private scope can be used to give individual permissions to data scientists, who would be responsible for installing and using just the packages they need. Because packages are installed on a per-user basis, packages installed by one user would not affect the work of other users who are using the same SQL Server database.