added links and metadata

Author: HeidiSteen

docs/advanced-analytics/install/sql-machine-learning-services-windows-install.md

@@ -1,9 +1,10 @@
 ---
 title: Install SQL Server Machine Learning Services (In-Database) on Windows | Microsoft Docs
+description: R in SQL Server or Python on SQL Server is available when you install SQL Server 2017 Machine Learning Services on Windows.
 ms.prod: sql
 ms.technology: machine-learning
 
-ms.date: 08/15/2018  
+ms.date: 09/08/2018  
 ms.topic: conceptual
 author: HeidiSteen
 ms.author: heidist
@@ -12,7 +13,7 @@ manager: cgronlun
 # Install SQL Server Machine Learning Services
 [!INCLUDE[appliesto-ss-xxxx-xxxx-xxx-md-winonly](../../includes/appliesto-ss-xxxx-xxxx-xxx-md-winonly.md)]
 
-Starting in SQL Server 2017, R and Python support for in-database analytics is provided in SQL Server Machine Learning Services, the successor to R Services feature introduced in SQL Server 2016. Function libraries are available in R and Python and run as external script on a database engine instance. 
+Starting in SQL Server 2017, R and Python support for in-database analytics is provided in SQL Server Machine Learning Services, the successor to [SQL Server R Services](../r/sql-r-services-windows.md) introduced in SQL Server 2016. Function libraries are available in R and Python and run as external script on a database engine instance. 
 
 This article explains how to install the machine learning component by running the [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] setup wizard, and following the on-screen prompts.
 

docs/advanced-analytics/install/sql-r-services-windows-install.md

@@ -1,9 +1,10 @@
 ---
 title: Install SQL Server 2016 R Services (In-Database) | Microsoft Docs
+description: R in SQL Server is available when you install SQL Server 2016 R Services on Windows.
 ms.prod: sql
 ms.technology: machine-learning
 
-ms.date: 04/15/2018
+ms.date: 09/08/2018
 ms.topic: conceptual
 author: HeidiSteen
 ms.author: heidist
@@ -14,9 +15,9 @@ manager: cgronlun
 
 This article explains how to install and configure **SQL Server 2016 R Services (In-Database)**. If you have SQL Server 2016, install this feature to enable execution of R code in SQL Server.
 
-## <a name="bkmk_prereqs"> </a> Pre-install checklist
+In SQL Server 2017, R integration is offered in [Machine Learning Services](../r/r-server-standalone.md), renamed with the addition of Python. If you want R in SQL Server and have SQL Server 2017 installation media, choose the Machine Learning Services R feature during setup. For more information, see  install [SQL Server 2017 Machine Learning Services (In-Database)](sql-machine-learning-services-windows-install.md). 
 
-+ SQL Server 2016 setup is required if you want to install R Services. If instead you have SQL Server 2017 installation media, you should install [SQL Server 2017 Machine Learning Services (In-Database)](sql-machine-learning-services-windows-install.md) to get R integration for that version of SQL Server.
+## <a name="bkmk_prereqs"> </a> Pre-install checklist
 
 + A database engine instance is required. You cannot install just R, although you can add it incrementally to an existing instance.
 
@@ -26,7 +27,7 @@ This article explains how to install and configure **SQL Server 2016 R Services
 
 + Do not install **Shared Features** > **R Server (Standalone)** on the same computer running an in-database instance. 
 
-+ Side-by-side installation with other versions of R and Python are possible because the SQL Server instance uses its own copies of the open-source R and Anaconda distributions. However, running code that uses R and Python on the SQL Server computer outside SQL Server can lead to various problems:
+  Side-by-side installation with other versions of R and Python are possible because the SQL Server instance uses its own copies of the open-source R and Anaconda distributions. However, running code that uses R and Python on the SQL Server computer outside SQL Server can lead to various problems:
 
   + You use a different library and different executable, and get different results, than you do when you are running in SQL Server.
   + R and Python scripts running in external libraries cannot be managed by SQL Server, leading to resource contention.