Merge pull request #207158 from v-rajagt/franlanglois

Link fixed.

Author: PRMerger9

articles/azure-cache-for-redis/cache-ml.md

@@ -33,7 +33,7 @@ Azure Cache for Redis is performant and scalable. When paired with an Azure Mach
 > * `model` - The registered model that will be deployed.
 > * `inference_config` - The inference configuration for the model.
 >
-> For more information on setting these variables, see [Deploy models with Azure Machine Learning](../machine-learning/how-to-deploy-and-where.md).
+> For more information on setting these variables, see [Deploy models with Azure Machine Learning](/azure/machine-learning/how-to-deploy-managed-online-endpoints).
 
 ## Create an Azure Cache for Redis instance
 
@@ -119,7 +119,7 @@ def run(data):
         return error
 ```
 
-For more information on entry script, see [Define scoring code.](../machine-learning/how-to-deploy-and-where.md?tabs=python#define-an-entry-script)
+For more information on entry script, see [Define scoring code.](/azure/machine-learning/how-to-deploy-managed-online-endpoints)
 
 * **Dependencies**, such as helper scripts or Python/Conda packages required to run the entry script or model
 
@@ -144,7 +144,7 @@ These entities are encapsulated into an **inference configuration**. The inferen
 
 For more information on environments, see [Create and manage environments for training and deployment](../machine-learning/how-to-use-environments.md).
 
-For more information on inference configuration, see [Deploy models with Azure Machine Learning](../machine-learning/how-to-deploy-and-where.md?tabs=python#define-an-inference-configuration).
+For more information on inference configuration, see [Deploy models with Azure Machine Learning](/azure/machine-learning/how-to-deploy-managed-online-endpoints).
 
 > [!IMPORTANT]
 > When deploying to Functions, you do not need to create a **deployment configuration**.
@@ -162,7 +162,7 @@ pip install azureml-contrib-functions
 To create the Docker image that is deployed to Azure Functions, use [azureml.contrib.functions.package](/python/api/azureml-contrib-functions/azureml.contrib.functions) or the specific package function for the trigger you want to use. The following code snippet demonstrates how to create a new package with an HTTP trigger from the model and inference configuration:
 
 > [!NOTE]
-> The code snippet assumes that `model` contains a registered model, and that `inference_config` contains the configuration for the inference environment. For more information, see [Deploy models with Azure Machine Learning](../machine-learning/how-to-deploy-and-where.md).
+> The code snippet assumes that `model` contains a registered model, and that `inference_config` contains the configuration for the inference environment. For more information, see [Deploy models with Azure Machine Learning](/azure/machine-learning/how-to-deploy-managed-online-endpoints).
 
 ```python
 from azureml.contrib.functions import package