Fixing links

garyericson · garyericson · commit b1b10525dbff · 2021-04-06T16:07:52.000-07:00
diff --git a/docs/machine-learning/deploy/modify-r-python-code-to-run-in-sql-server.md b/docs/machine-learning/deploy/modify-r-python-code-to-run-in-sql-server.md
@@ -142,7 +142,7 @@ How much you change your code depends on whether you intend to submit the code f
 + If you have complex R code, use the R package **sqlrutils** to convert your code. This package is designed to help experienced R users write good stored procedure code.
   You rewrite your R code as a single function with clearly defined inputs and outputs, then use the **sqlrutils** package to generate the input and outputs in the correct format. The **sqlrutils** package generates the complete stored procedure code for you, and can also register the stored procedure in the database.
 
-  For more information and examples, see [sqlrutils (SQL)](ref-r-sqlrutils.md).
+  For more information and examples, see [sqlrutils (SQL)](../r/ref-r-sqlrutils.md).
 
 ### Integrate with other workflows
 
diff --git a/docs/machine-learning/deploy/operationalizing-your-r-code.md b/docs/machine-learning/deploy/operationalizing-your-r-code.md
@@ -29,8 +29,8 @@ A more comprehensive example of deploying R code into production by using stored
 
 Converting your R code in SQL is easier if some optimizations are done beforehand in the R or Python code. These include avoiding data types that cause problems, avoiding unnecessary data conversions, and rewriting the R code as a single function call that can be easily parameterized. For more information, see:
 
-+ [R libraries and data types](r-libraries-and-data-types.md)
-+ [Use sqlrutils helper functions](ref-r-sqlrutils.md)
++ [R libraries and data types](../r/r-libraries-and-data-types.md)
++ [Use sqlrutils helper functions](../r/ref-r-sqlrutils.md)
 
 ## Integrate R and Python with applications
 
@@ -54,7 +54,7 @@ The following tutorial provides an example of scoring using a stored procedure i
 
 ## Boost performance and scale
 
-Although the open-source R language has known limitations with regards to large data sets, the [RevoScaleR package APIs](ref-r-revoscaler.md) included with SQL Server Machine Learning Service can operate on large datasets and benefit from multi-threaded, multi-core, multi-process in-database computations.
+Although the open-source R language has known limitations with regards to large data sets, the [RevoScaleR package APIs](../r/ref-r-revoscaler.md) included with SQL Server Machine Learning Service can operate on large datasets and benefit from multi-threaded, multi-core, multi-process in-database computations.
 
 If your R solution uses complex aggregations or involves large datasets, you can leverage SQL Server's highly efficient in-memory aggregations and columnstore indexes, and let the R code handle the statistical computations and scoring.
 
