Merge pull request #14331 from MicrosoftDocs/master

3/31 AM Publish

Author: huypub

.openpublishing.redirection.json

@@ -16586,9 +16586,9 @@
       "redirect_document_id": false
     },
     {
-    "source_path": "docs/database-engine/deprecated-database-engine-features-in-sql-server-version-15.md",
-    "redirect_url": "/sql/database-engine/deprecated-database-engine-features-in-sql-server-2019",
+    "source_path": "docs/database-engine/deprecated-database-engine-features-in-sql-server-2019.md",
+    "redirect_url": "/sql/database-engine/deprecated-database-engine-features-in-sql-server-version-15",
     "redirect_document_id": true
-  }
+    }
   ]
 }

docs/2014/includes/appliesto-ss2008-xxxx-xxxx-xxx-md.md

@@ -1 +1 @@
-<Token>**THIS TOPIC APPLIES TO:**![yes](media/yes.png)SQL Server (starting with 2008)![no](media/no.png)Azure SQL Database![no](media/no.png)Azure SQL Data Warehouse ![no](media/no.png)Parallel Data Warehouse </Token>
+<Token>**THIS TOPIC APPLIES TO:**![yes](media/yes-icon.png)SQL Server (starting with 2008)![no](media/no-icon.png)Azure SQL Database![no](media/no-icon.png)Azure SQL Data Warehouse ![no](media/no-icon.png)Parallel Data Warehouse </Token>

docs/2014/includes/media/no.png renamed to docs/2014/includes/media/no-icon.png

@@ -1,4 +1,4 @@
-**THIS TOPIC APPLIES TO:** ![yes](media/yes.png "yes")SQL Server \(starting with 2008\)
-![no](media/no.png "no")Azure SQL Database
-![no](media/no.png "no")Azure SQL Data Warehouse
-![no](media/no.png "no")Parallel Data Warehouse
+**THIS TOPIC APPLIES TO:** ![yes](media/yes-icon.png "yes")SQL Server \(starting with 2008\)
+![no](media/no-icon.png "no")Azure SQL Database
+![no](media/no-icon.png "no")Azure SQL Data Warehouse
+![no](media/no-icon.png "no")Parallel Data Warehouse

docs/2014/includes/media/yes.png renamed to docs/2014/includes/media/yes-icon.png

@@ -4,7 +4,7 @@ description: Use rxPredict, or sp_rxPredict for real-time scoring, or PREDICT T-
 ms.prod: sql
 ms.technology: machine-learning
 
-ms.date: 08/30/2018  
+ms.date: 03/30/2020
 ms.topic: conceptual
 author: dphansen
 ms.author: davidph
@@ -23,7 +23,7 @@ The following table summarizes the scoring frameworks for forecasting and predic
 | Methodology           | Interface         | Library requirements | Processing speeds |
 |-----------------------|-------------------|----------------------|----------------------|
 | Extensibility framework | [rxPredict (R)](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/rxpredict) <br/>[rx_predict (Python)](https://docs.microsoft.com/machine-learning-server/python-reference/revoscalepy/rx-predict) | None. Models can be based on any R or Python function | Hundreds of milliseconds. <br/>Loading a runtime environment has a fixed cost, averaging three to six hundred milliseconds, before any new data is scored. |
-| [Real-time scoring CLR extension](../real-time-scoring.md) | [sp_rxPredict](https://docs.microsoft.com//sql/relational-databases/system-stored-procedures/sp-rxpredict-transact-sql) on a serialized model | R: RevoScaleR, MicrosoftML <br/>Python: revoscalepy, microsoftml | Tens of milliseconds, on average. |
+| [Real-time scoring CLR extension](../real-time-scoring.md) | [sp_rxPredict](https://docs.microsoft.com/sql/relational-databases/system-stored-procedures/sp-rxpredict-transact-sql) on a serialized model | R: RevoScaleR, MicrosoftML <br/>Python: revoscalepy, microsoftml | Tens of milliseconds, on average. |
 | [Native scoring C++ extension](../sql-native-scoring.md) | [PREDICT T-SQL function](https://docs.microsoft.com/sql/t-sql/queries/predict-transact-sql) on a serialized model | R: RevoScaleR <br/>Python: revoscalepy | Less than 20 milliseconds, on average. | 
 
 Speed of processing and not substance of the output is the differentiating feature. Assuming the same functions and inputs, the scored output should not vary based on the approach you use.
@@ -80,7 +80,7 @@ To use a model with either of the fast scoring options, save the model using a s
 
 **Using SQL**
 
-From SQL code, you can train the model using [sp_execute_external_script](https://docs.microsoft.com//sql/relational-databases/system-stored-procedures/sp-execute-external-script-transact-sql), and directly insert the trained models into a table, in a column of type **varbinary(max)**. For a simple example, see [Create a preditive model in R](../tutorials/quickstart-r-train-score-model.md)
+From SQL code, you can train the model using [sp_execute_external_script](https://docs.microsoft.com/sql/relational-databases/system-stored-procedures/sp-execute-external-script-transact-sql), and directly insert the trained models into a table, in a column of type **varbinary(max)**. For a simple example, see [Create a preditive model in R](../tutorials/quickstart-r-train-score-model.md)
 
 **Using R**
 

docs/2014/includes/tsql-appliesto-ss2008-xxxx-xxxx-xxx-md.md

@@ -4,7 +4,7 @@ description: Generate predictions using sp_rxPredict, scoring data inputs agains
 ms.prod: sql
 ms.technology: machine-learning
 
-ms.date: 07/26/2019
+ms.date: 03/30/2020
 ms.topic: conceptual
 author: dphansen
 ms.author: davidph
@@ -14,7 +14,7 @@ monikerRange: ">=sql-server-2016||>=sql-server-linux-ver15||=sqlallproducts-allv
 # Real-time scoring with sp_rxPredict in SQL Server machine learning
 [!INCLUDE[appliesto-ss-xxxx-xxxx-xxx-md](../includes/appliesto-ss-xxxx-xxxx-xxx-md.md)]
 
-Real-time scoring uses the [sp_rxPredict](https://docs.microsoft.com//sql/relational-databases/system-stored-procedures/sp-rxpredict-transact-sql) system stored procedure and the CLR extension capabilities in SQL Server for high-performance predictions or scores in forecasting workloads. Real-time scoring is language-agnostic and executes with no dependencies on R or Python run times. Assuming a model created and trained using Microsoft functions, and then serialized to a binary format in SQL Server, you can use real-time scoring to generate predicted outcomes on new data inputs on SQL Server instances that do not have the R or Python add-on installed.
+Real-time scoring uses the [sp_rxPredict](https://docs.microsoft.com/sql/relational-databases/system-stored-procedures/sp-rxpredict-transact-sql) system stored procedure and the CLR extension capabilities in SQL Server for high-performance predictions or scores in forecasting workloads. Real-time scoring is language-agnostic and executes with no dependencies on R or Python run times. Assuming a model created and trained using Microsoft functions, and then serialized to a binary format in SQL Server, you can use real-time scoring to generate predicted outcomes on new data inputs on SQL Server instances that do not have the R or Python add-on installed.
 
 ## How real-time scoring works
 
@@ -27,7 +27,7 @@ Real-time scoring is a multi-step process:
 1. The stored procedure that does scoring must be enabled on a per-database basis.
 2. You load the pre-trained model in binary format.
 3. You provide new input data to be scored, either tabular or single rows, as input to the model.
-4. To generate scores, call the [sp_rxPredict](https://docs.microsoft.com//sql/relational-databases/system-stored-procedures/sp-rxpredict-transact-sql) stored procedure.
+4. To generate scores, call the [sp_rxPredict](https://docs.microsoft.com/sql/relational-databases/system-stored-procedures/sp-rxpredict-transact-sql) stored procedure.
 
 ## Prerequisites
 
@@ -146,9 +146,9 @@ You must enable this feature for each database that you want to use for scoring.
 
 3. RegisterRExt.exe creates the following objects:
 
-	+ Trusted assemblies
-	+ The stored procedure `sp_rxPredict`
-	+ A new database role, `rxpredict_users`. The database administrator can use this role to grant permission to users who use the real-time scoring functionality.
+    + Trusted assemblies
+    + The stored procedure `sp_rxPredict`
+    + A new database role, `rxpredict_users`. The database administrator can use this role to grant permission to users who use the real-time scoring functionality.
 
 4. Add any users who need to run `sp_rxPredict` to the new role.