Formatting and minor fixes.

TimShererWithAquent · TimShererWithAquent · commit 33f5dee9a293 · 2024-08-16T09:10:14.000-07:00
diff --git a/articles/machine-learning/how-to-use-mlflow-azure-databricks.md b/articles/machine-learning/how-to-use-mlflow-azure-databricks.md
@@ -73,7 +73,7 @@ To link your Azure Databricks workspace to a new or existing Azure Machine Learn
 1. Navigate to your Azure Databricks workspace **Overview** page.
 1. Select **Link Azure Machine Learning workspace**.
 
-   :::image type="content" source="./media/how-to-use-mlflow-azure-databricks/link-workspaces.png" alt-text="Screenshot shows the Link Azure Databricks and Azure Machine Learning workspaces option.":::
+   :::image type="content" source="./media/how-to-use-mlflow-azure-databricks/link-workspaces.png" lightbox="./media/how-to-use-mlflow-azure-databricks/link-workspaces.png" alt-text="Screenshot shows the Link Azure Databricks and Azure Machine Learning workspaces option.":::
 
 After you link your Azure Databricks workspace with your Azure Machine Learning workspace, MLflow Tracking is automatically tracked in the following places:
 
@@ -109,71 +109,71 @@ Configure the MLflow tracking URI to point exclusively to Azure Machine Learning
 
 1. Get the tracking URI for your workspace:
 
-# [Azure CLI](#tab/cli)
+   # [Azure CLI](#tab/cli)
 
    [!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
 
-   1. Sign in and configure your workspace:
+1. Sign in and configure your workspace:
 
    ```bash
    az account set --subscription <subscription>
    az configure --defaults workspace=<workspace> group=<resource-group> location=<location> 
    ```
 
-   1. You can get the tracking URI using the `az ml workspace` command:
+1. You can get the tracking URI using the `az ml workspace` command:
 
    ```bash
    az ml workspace show --query mlflow_tracking_uri
    ```
 
-# [Python](#tab/python)
+   # [Python](#tab/python)
 
-[!INCLUDE [sdk v2](includes/machine-learning-sdk-v2.md)]
+   [!INCLUDE [sdk v2](includes/machine-learning-sdk-v2.md)]
 
    You can get the Azure Machine Learning MLflow tracking URI using the [Azure Machine Learning SDK v2 for Python](concept-v2.md). Ensure you have the library `azure-ai-ml` installed in the compute you're using. The following sample gets the unique MLFLow tracking URI associated with your workspace.
 
-   1. Sign in into your workspace using the `MLClient`. The easier way to do that is by using the workspace config file:
+1. Sign in into your workspace using the `MLClient`. The easier way to do that is by using the workspace config file:
 
-      ```python
-      from azure.ai.ml import MLClient
-      from azure.identity import DefaultAzureCredential
+   ```python
+   from azure.ai.ml import MLClient
+   from azure.identity import DefaultAzureCredential
 
       ml_client = MLClient.from_config(credential=DefaultAzureCredential())
-      ```
+   ```
 
-      > [!TIP]
-      > To download the workspace configuration file:
-      >
-      > 1. Navigate to [Azure Machine Learning studio](https://ml.azure.com)
-      > 1. Click on the upper-right corner of the page -> Download config file.
-      > 1. Save the file `config.json` in the same directory where you are working on.
+   > [!TIP]
+   > To download the workspace configuration file:
+   >
+   > 1. Navigate to [Azure Machine Learning studio](https://ml.azure.com)
+   > 1. Click on the upper-right corner of the page -> Download config file.
+   > 1. Save the file `config.json` in the same directory where you are working on.
 
-      Alternatively, you can use the subscription ID, resource group name, and workspace name to get it:
+   Alternatively, you can use the subscription ID, resource group name, and workspace name to get it:
 
-      ```python
-      from azure.ai.ml import MLClient
-      from azure.identity import DefaultAzureCredential
+   ```python
+   from azure.ai.ml import MLClient
+   from azure.identity import DefaultAzureCredential
 
-      #Enter details of your Azure Machine Learning workspace
-      subscription_id = '<SUBSCRIPTION_ID>'
-      resource_group = '<RESOURCE_GROUP>'
-      workspace_name = '<WORKSPACE_NAME>'
+   #Enter details of your Azure Machine Learning workspace
+   subscription_id = '<SUBSCRIPTION_ID>'
+   resource_group = '<RESOURCE_GROUP>'
+   workspace_name = '<WORKSPACE_NAME>'
 
-      ml_client = MLClient(credential=DefaultAzureCredential(),
-                              subscription_id=subscription_id, 
-                              resource_group_name=resource_group)
-      ```
+   ml_client = MLClient(credential=DefaultAzureCredential(),
+                           subscription_id=subscription_id, 
+                           resource_group_name=resource_group)
+   ```
 
-      > [!IMPORTANT]
-      > `DefaultAzureCredential` tries to pull the credentials from the available context. If you want to specify credentials in a different way, for instance using the web browser in an interactive way, you can use `InteractiveBrowserCredential` or any other method available in [`azure.identity`](https://pypi.org/project/azure-identity/) package.
+   > [!IMPORTANT]
+   > `DefaultAzureCredential` tries to pull the credentials from the available context. If you want to specify credentials in a different way, for instance using the web browser in an interactive way, you can use `InteractiveBrowserCredential` or any other method available in [`azure.identity`](https://pypi.org/project/azure-identity/) package.
 
-   1. Get the Azure Machine Learning Tracking URI:
+1. Get the Azure Machine Learning Tracking URI:
 
-      ```python
-      mlflow_tracking_uri = ml_client.workspaces.get(ml_client.workspace_name).mlflow_tracking_uri
-      ```
+   ```python
+   mlflow_tracking_uri = ml_client.workspaces.get(ml_client.workspace_name).mlflow_tracking_uri
+   ```
 
-# [Studio](#tab/studio)
+   # [Studio](#tab/studio)
 
    Use the Azure Machine Learning portal to get the tracking URI:
 
@@ -182,7 +182,7 @@ Configure the MLflow tracking URI to point exclusively to Azure Machine Learning
    1. Select **View all properties in Azure Portal**.
    1. On the **Essentials** section, find the property **MLflow tracking URI**.
 
-# [Manually](#tab/manual)
+   # [Manually](#tab/manual)
 
    You can construct the Azure Machine Learning Tracking URI using the subscription ID, region of where the resource is deployed, resource group name, and workspace name. The following code sample shows how:
 
@@ -198,11 +198,11 @@ Configure the MLflow tracking URI to point exclusively to Azure Machine Learning
    mlflow_tracking_uri = f"azureml://{region}.api.azureml.ms/mlflow/v1.0/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.MachineLearningServices/workspaces/{workspace_name}"
    ```
 
----
+   ---
 
 1. Configuring the tracking URI:
 
-   # [Using MLflow SDK](#tab/mlflow)
+   # [Use MLflow SDK](#tab/mlflow)
 
    Then the method [`set_tracking_uri()`](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.set_tracking_uri) points the MLflow tracking URI to that URI.
 
@@ -212,40 +212,40 @@ Configure the MLflow tracking URI to point exclusively to Azure Machine Learning
    mlflow.set_tracking_uri(mlflow_tracking_uri)
    ```
 
-   # [Using environment variables](#tab/environ)
+   # [Use environment variables](#tab/environ)
 
    You can set the MLflow environment variables [MLFLOW_TRACKING_URI](https://mlflow.org/docs/latest/tracking.html#logging-to-a-tracking-server) in your compute to make any interaction with MLflow in the compute to point by default to Azure Machine Learning.
 
    ```bash
    MLFLOW_TRACKING_URI=$(az ml workspace show --query mlflow_tracking_uri | sed 's/"//g') 
    ```
 
-    ---
+   ---
 
-    > [!TIP]
-    > When working with shared environments, like an Azure Databricks cluster, Azure Synapse Analytics cluster, or similar, you can set the environment variable `MLFLOW_TRACKING_URI` at the cluster level. This approach allows you to automatically configure the MLflow tracking URI to point to Azure Machine Learning for all the sessions that run in the cluster rather than to do it on a per-session basis.
-    >
-    > :::image type="content" source="./media/how-to-use-mlflow-azure-databricks/env.png" alt-text="Screenshot shows Acvanced options where you can configure the environment variables in an Azure Databricks cluster.":::
-    >
-    > After you configure the environment variable, any experiment running in such cluster is tracked in Azure Machine Learning.
+> [!TIP]
+> When working with shared environments, like an Azure Databricks cluster, Azure Synapse Analytics cluster, or similar, you can set the environment variable `MLFLOW_TRACKING_URI` at the cluster level. This approach allows you to automatically configure the MLflow tracking URI to point to Azure Machine Learning for all the sessions that run in the cluster rather than to do it on a per-session basis.
+>
+> :::image type="content" source="./media/how-to-use-mlflow-azure-databricks/env.png" alt-text="Screenshot shows Acvanced options where you can configure the environment variables in an Azure Databricks cluster.":::
+>
+> After you configure the environment variable, any experiment running in such cluster is tracked in Azure Machine Learning.
 
 #### Configure authentication
 
 After you configure tracking, configure how to authenticate to the associated workspace. By default, the Azure Machine Learning plugin for MLflow opens a browser to interactively prompt for credentials. For other ways to configure authentication for MLflow in Azure Machine Learning workspaces, see [Configure MLflow for Azure Machine Learning: Configure authentication](how-to-use-mlflow-configure-tracking.md#configure-authentication).
 
 [!INCLUDE [configure-mlflow-auth](includes/machine-learning-mlflow-configure-auth.md)]
 
-#### Experiment's names in Azure Machine Learning
+#### Experiment names in Azure Machine Learning
 
-When you configure MLflow to exclusively track experiments in Azure Machine Learning workspace, the experiment's naming convention has to follow the one used by Azure Machine Learning. In Azure Databricks, experiments are named with the path to where the experiment is saved, for instance `/Users/alice@contoso.com/iris-classifier`. However, in Azure Machine Learning, you provide the experiment name directly. The same experiment would be named `iris-classifier` directly:
+When you configure MLflow to exclusively track experiments in Azure Machine Learning workspace, the experiment naming convention has to follow the one used by Azure Machine Learning. In Azure Databricks, experiments are named with the path to where the experiment is saved, for instance `/Users/alice@contoso.com/iris-classifier`. However, in Azure Machine Learning, you provide the experiment name directly. The same experiment would be named `iris-classifier` directly:
 
 ```python
 mlflow.set_experiment(experiment_name="experiment-name")
 ```
 
 #### Tracking parameters, metrics and artifacts
 
-After this configuration, you can use MLflow in Azure Databricks in the same way as you're used to. For details see [Log & view metrics and log files](how-to-log-view-metrics.md).
+After this configuration, you can use MLflow in Azure Databricks in the same way as you're used to. For more information, see [Log & view metrics and log files](how-to-log-view-metrics.md).
 
 ## Logging models with MLflow
 
diff --git a/articles/machine-learning/includes/machine-learning-mlflow-configure-auth.md b/articles/machine-learning/includes/machine-learning-mlflow-configure-auth.md
@@ -34,4 +34,6 @@ export AZURE_CLIENT_SECRET="<AZURE_CLIENT_SECRET>"
 ---
 
 > [!TIP]
-> When working on shared environments, we recommend that you configure these environment variables at the compute. As a best practice, manage them as secrets in an instance of Azure Key Vault. For instance, in Azure Databricks you can use secrets in environment variables as follows in the cluster configuration: `AZURE_CLIENT_SECRET={{secrets/<scope-name>/<secret-name>}}`. For more information about implementing this approach in Azure Databricks, see [Reference a secret in an environment variable](/azure/databricks/security/secrets/secrets#reference-a-secret-in-an-environment-variable) or refer to documentation for your platform.
+> When working on shared environments, we recommend that you configure these environment variables at the compute. As a best practice, manage them as secrets in an instance of Azure Key Vault.
+>
+> For instance, in Azure Databricks you can use secrets in environment variables as follows in the cluster configuration: `AZURE_CLIENT_SECRET={{secrets/<scope-name>/<secret-name>}}`. For more information about implementing this approach in Azure Databricks, see [Reference a secret in an environment variable](/azure/databricks/security/secrets/secrets#reference-a-secret-in-an-environment-variable) or refer to documentation for your platform.
