Update how-to-deploy-and-where.md

Author: jpe316

articles/machine-learning/how-to-deploy-and-where.md

@@ -62,7 +62,7 @@ The following code shows how to connect to an Azure Machine Learning workspace b
 A registered model is a logical container for one or more files that make up your model. For example, if you have a model that's stored in multiple files, you can register them as a single model in the workspace. After you register the files, you can then download or deploy the registered model and receive all the files that you registered.
 
 > [!TIP]
-> When you register a model, you provide the path of either a cloud location (from a training run) or a local directory. This path is just to locate the files for upload as part of the registration process. It doesn't need to match the path used in the entry script. For more information, see [Locate model files in your entry script](#locate-model-files-in-your-entry-script).
+> When you register a model, you provide the path of either a cloud location (from a training run) or a local directory. This path is just to locate the files for upload as part of the registration process. It doesn't need to match the path used in the entry script. For more information, see [Locate model files in your entry script](#load-model-files-in-your-entry-script).
 
 Machine learning models are registered in your Azure Machine Learning workspace. The model can come from Azure Machine Learning or from somewhere else. When registering a model, you can optionally provide metadata about the model. The `tags` and `properties` dictionaries that you apply to a model registration can then be used to filter models.