update bookmark

Author: sdgilley

articles/machine-learning/how-to-r-modify-script-for-production.md

@@ -129,7 +129,7 @@ saveRDS(myobject, file = file.path(args$output,"myobject.rds"))
 
 ```
 
-## <a name="crate"></a>`crate` your models with the `carrier` package
+## `crate` your models with the `carrier` package
 
 The [R MLflow API documentation](https://MLflow.org/docs/latest/models.html#r-function-crate) specifies that your R models need to be of the `crate` _model flavor_.  
 

articles/machine-learning/how-to-r-train-model.md

@@ -44,7 +44,7 @@ Create this folder structure for your project:
 > [!IMPORTANT]
 > All source code goes in the `src` directory.
 
-* The **r-source.R** file is the R script that you adapted to run in production. Make sure you use the source code tips from the [adapting article](how-to-r-modify-script-for-production.md#source-the-r-script.md#crate), especially to crate and log your model.
+* The **r-source.R** file is the R script that you adapted to run in production. Make sure you follow the steps to [crate and log your model](how-to-r-modify-script-for-production.md#source-the-r-script.md#crate-your-models-with-the-carrier-package) in this script.
 * The **azureml_utils.R** file is necessary. Use [this source code](how-to-r-modify-script-for-production.md#source-the-azureml_utilsr-helper-script) for the contents of the file.