Merge pull request #204105 from santiagxf/santiagxf/aml-mlflow-deployment-fix

Aml mlflow deployment fix

Author: PRMerger16

articles/machine-learning/how-to-use-mlflow-azure-databricks.md

@@ -56,7 +56,7 @@ To link your ADB workspace to a new or existing Azure Machine Learning workspace
  ![Link Azure DB and Azure Machine Learning workspaces](./media/how-to-use-mlflow-azure-databricks/link-workspaces.png)
 
 > [!WARNING]
-> Dual-tracking in a [private link enabled Azure Machine Learning workspace](how-to-configure-private-link.md) is not supported by the moment. Configure [exclusive tracking with your Azure Machine Learning workspace](#tracking-exclusively-on-azure-machine-learning-workspace) instead. Notice that for this configuration to work, you have to [deploy Azure Databricks in your own network (VNet injection)](/azure/databricks/administration-guide/cloud-configurations/azure/vnet-inject.md).
+> Dual-tracking in a [private link enabled Azure Machine Learning workspace](how-to-configure-private-link.md) is not supported by the moment. Configure [exclusive tracking with your Azure Machine Learning workspace](#tracking-exclusively-on-azure-machine-learning-workspace) instead.
 
 After you link your Azure Databricks workspace with your Azure Machine Learning workspace, MLflow Tracking is automatically set to be tracked in all of the following places:
 
@@ -83,6 +83,9 @@ with mlflow.start_run():
 
 If you prefer to manage your tracked experiments in a centralized location, you can set MLflow tracking  to **only** track in your Azure Machine Learning workspace. This configuration has the advantage of enabling easier path to deployment using Azure Machine Learning deployment options.
 
+> [!WARNING]
+> For [private link enabled Azure Machine Learning workspace](how-to-configure-private-link.md), you have to [deploy Azure Databricks in your own network (VNet injection)](/azure/databricks/administration-guide/cloud-configurations/azure/vnet-inject.md) to ensure proper connectivity.
+
 You have to configure the MLflow tracking URI to point exclusively to Azure Machine Learning, as it is demonstrated in the following example:
 
    # [Using the Azure ML SDK v2](#tab/sdkv2)

articles/machine-learning/how-to-use-mlflow-azure-synapse.md

@@ -46,7 +46,7 @@ To install libraries on your dedicated cluster in Azure Synapse Analytics:
 
 5. Click the three dots next to the cluster name, and select **Packages**.
 
-    ![install mlflow packages in Azure Synapse Analytics](/articles/machine-learning/media/how-to-use-mlflow-azure/install-packages.png)
+    ![install mlflow packages in Azure Synapse Analytics](media/how-to-use-mlflow-azure/install-packages.png)
 
 6. On the **Requirements files** section, click on **Upload**.