Added info about ML Services for Azure SQL DB.

Author: davidpeterh

docs/advanced-analytics/what-is-sql-server-machine-learning.md

@@ -4,7 +4,7 @@ description: R in SQL Server and Python in SQL Server, integrating with relation
 ms.prod: sql
 ms.technology: machine-learning
 
-ms.date: 09/10/2018  
+ms.date: 11/06/2018  
 ms.topic: overview
 author: HeidiSteen
 ms.author: heidist
@@ -15,7 +15,7 @@ manager: cgronlun
 
 SQL Server 2017 Machine Learning Services is an add-on to a database engine instance, used for executing R and Python code on SQL Server. Code runs in an extensibility framework, isolated from core engine processes, but fully available to relational data as stored procedures, as T-SQL script containing R or Python statements, or as R or Python code containing T-SQL. 
 
-If you previously used [SQL Server 2016 R Services](r/sql-server-r-services.md), Machine Learning Services in SQL Server 2017 is the next generation of R support, with updated versions of base R, RevoScaleR, MicrosoftML, and other libraries introduced in 2016.
+If you previously used [SQL Server 2016 R Services](r/sql-server-r-services.md), Machine Learning Services in SQL Server 2017 is the next generation of R support, with updated versions of base R, RevoScaleR, MicrosoftML, and other libraries introduced in 2016. If you are using Azure SQL Database, [Machine Learning Services (with R)]((https://docs.microsoft.com/azure/sql-database/sql-database-connect-query-r)) is in public preview.
 
 The key value proposition of Machine Learning Services is the power of its enterprise R and Python packages to deliver advanced analytics at scale, and the ability to bring calculations and processing to where the data resides, eliminating the need to pull data across the network.
 

docs/advanced-analytics/what-s-new-in-sql-server-machine-learning-services.md

@@ -4,7 +4,7 @@ description: New feature announcements for each release of SQL Server 2016 R Ser
 ms.prod: sql
 ms.technology: machine-learning
 
-ms.date: 09/24/2018  
+ms.date: 11/06/2018  
 ms.topic: conceptual
 author: HeidiSteen
 ms.author: heidist
@@ -98,9 +98,9 @@ On Linux, SQL Server 2017 does not have R or Python integration, but you can use
 
 <a name="azure-sql-database-roadmap"></a>
 
-## Azure SQL Database roadmap
+## Machine Learning Services in Azure SQL Database
 
-There is currently no support for Machine Learning Services (R and Python) in Azure SQL Database. Support is on the roadmap but there is no projected release date at this time.  
+Machine Learning Services (with R) in Azure SQL Database is in public preview. For more information, see [Quickstart: Use Machine Learning Services (with R) in Azure SQL Database (preview)](https://docs.microsoft.com/azure/sql-database/sql-database-connect-query-r).
 
 ## Next steps
 

docs/t-sql/queries/predict-transact-sql.md

@@ -1,7 +1,7 @@
 ---
 title: "PREDICT (Transact-SQL) | Microsoft Docs"
 ms.custom: ""
-ms.date: "07/02/2018"
+ms.date: "11/06/2018"
 ms.prod: sql
 ms.prod_service: "sql-database"
 ms.reviewer: ""
@@ -65,9 +65,6 @@ The PARAMETERS parameter is used to specify optional user-defined parameters use
 
 The name of each parameter is specific to the model type. For example, the [rxPredict](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/rxpredict) function in RevoScaleR supports the parameter `@computeResiduals`, which indicates whether residuals should be computed when scoring a logistic regression model. If you are calling a compatible model, you could pass that parameter name and a TRUE or FALSE value to the `PREDICT` function.
 
-> [!NOTE]
-> This option does not work in pre-release versions of SQL Server 2017.
-
 **WITH ( <result_set_definition> )**
 
 The WITH clause is used to specify the schema of the output returned by the `PREDICT` function.
@@ -167,9 +164,6 @@ END;
 
 ### Creating an R model and generating scores using optional model parameters
 
-> [!NOTE]
-> Use of the parameters argument is not supported in Release Candidate 1.
-
 This example assumes that you have created a logistic regression model fitted with a covariance matrix, using a call to RevoScaleR such as this:
 
 ```R