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Author: cdpark

articles/machine-learning/toc.yml

@@ -301,6 +301,8 @@ items:
     - name: Connect to data (UI)
       displayName: blob, get, fileshare, access, mount, download, data lake, datastore, dataset, data set
       href: ./v1/how-to-connect-data-ui.md
+    - name: Enable log metrics
+      href: ./v1/how-to-track-designer-experiments.md
     - name: Manage & consume data
       items:
         - name: Train with datasets

articles/machine-learning/v1/how-to-deploy-model-designer.md

@@ -17,7 +17,7 @@ ms.custom: UpdateFrequency5, deploy, studio, designer
 
 [!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
-In this article, you learn how to deploy a designer model as an online (real-time) endpoint in Azure Machine Learning studio.
+In this article, you learn how to deploy a designer model as a real-time, online endpoint in Azure Machine Learning studio.
 
 Once registered or downloaded, you can use designer trained models just like any other model. Exported models can be deployed in use cases such as internet of things (IoT) and local deployments.
 
@@ -28,9 +28,9 @@ Deployment in the studio consists of the following steps:
 1. (Optional) Configure the entry script.
 1. Deploy the model to a compute target.
 
-You can also deploy models directly in the designer to skip model registration and file download steps. This can be useful for rapid deployment. For more information, see [Deploy a model with the designer](tutorial-designer-automobile-price-deploy.md).
+You can also deploy models directly in the designer to skip model registration and file-download steps. This can be useful for rapid deployment. For more information, see [Tutorial: Deploy a machine learning model using designer](tutorial-designer-automobile-price-deploy.md).
 
-Models trained in the designer can also be deployed through the SDK or command-line interface (CLI). For more information, see [Deploy your existing model with Azure Machine Learning](how-to-deploy-and-where.md).
+Models trained in the designer can also be deployed through the SDK or command-line interface (CLI). For more information, see [Deploy machine learning models to Azure](how-to-deploy-and-where.md).
 
 ## Prerequisites
 
@@ -58,7 +58,7 @@ After the training pipeline completes, register the trained model to your Azure
 
 1. Select the **Outputs + logs** tab in the details pane.
 
-1. Select **+ Register model**.
+1. Select **Register model**.
 
     :::image type="content" source="./media/how-to-deploy-model-designer/train-model-right-pane.png" alt-text="Screenshot of right pane of Train Model component." lightbox="./media/how-to-deploy-model-designer/train-model-right-pane.png":::
 
@@ -278,7 +278,7 @@ def run(data):
 
 For **Wide & Deep Recommender** and **Vowpal Wabbit** models, you can configure the scoring mode parameter by using the following methods:
 
-- The parameter names are the lowercase and underscore combinations of parameter names for [Score Vowpal Wabbit Model](../algorithm-module-reference/score-vowpal-wabbit-model.md) and [Score Wide and Deep Recommender](../algorithm-module-reference/score-wide-and-deep-recommender.md);
+- The parameter names are the lowercase and underscore combinations of parameter names for [Score Vowpal Wabbit Model](../algorithm-module-reference/score-vowpal-wabbit-model.md) and [Score Wide and Deep Recommender](../algorithm-module-reference/score-wide-and-deep-recommender.md).
 - Mode-type parameter values are strings of the corresponding option names. Take **Recommender prediction kind** in the preceding codes as an example, the value can be `'Rating Prediction'`or `'Item Recommendation'`. Other values aren't allowed.
 
 For **SVD Recommender** trained model, the parameter names and values might be less obvious, and you can look up the following tables to decide how to set parameters.

articles/machine-learning/v1/how-to-designer-import-data.md

@@ -17,17 +17,17 @@ ms.custom: UpdateFrequency5, designer
 
 [!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
-In this article, you learn how to import your own data in the designer to create custom solutions. There are two ways you can import data into the designer: 
+In this article, you learn how to import your own data into the Azure Machine Learning designer to create custom solutions. There are two ways you can import data into the designer: 
 
-* **Azure Machine Learning datasets**: Register [datasets](concept-data.md) in Azure Machine Learning to enable advanced features that help you manage your data.
+* **Azure Machine Learning datasets**: Register [datasets](concept-data.md#reference-data-in-storage-with-datasets) in Azure Machine Learning to enable advanced features that help you manage your data.
 * **Import Data component**: Use the [Import Data](../algorithm-module-reference/import-data.md) component to directly access data from online data sources.
 
 > [!IMPORTANT]
 > If you don't see graphical elements mentioned in this document, such as buttons in studio or designer, you might not have the right level of permissions to the workspace. Contact your Azure subscription administrator to verify that you have been granted the correct level of access. For more information, see [Manage users and roles](../how-to-assign-roles.md).
 
 ## Use Azure Machine Learning datasets
 
-We recommend that you use [datasets](concept-data.md) to import data into the designer. When you register a dataset, you can take full advantage of advanced data features like [versioning and tracking](how-to-version-track-datasets.md) and [data monitoring](how-to-monitor-datasets.md).
+We recommend that you use [datasets](concept-data.md#reference-data-in-storage-with-datasets) to import data into the designer. When you register a dataset, you can take full advantage of advanced data features like [versioning and tracking](how-to-version-track-datasets.md) and [data monitoring](how-to-monitor-datasets.md).
 
 ### Register a dataset
 
@@ -62,9 +62,9 @@ If you register a file dataset, the output port type of the dataset is **AnyDire
 
 ### Limitations 
 
-- Currently you can only visualize tabular dataset in the designer. If you register a file dataset outside designer, you can't visualize it in the designer canvas.
-- Currently the designer only supports preview outputs which are stored in **Azure blob storage**. You can check and change your output datastore in the **Output settings** under **Parameters** tab in the right panel of the component.
-- If your data is stored in virtual network and you want to preview, you need to enable workspace managed identity of the datastore.
+- Currently you can only visualize a tabular dataset in the designer. If you register a file dataset outside designer, you can't visualize it in the designer canvas.
+- Currently the designer only supports preview outputs that are stored in **Azure blob storage**. You can check and change your output datastore in the **Output settings** under **Parameters** tab in the right panel of the component.
+- If your data is stored in a virtual network and you want to preview, you need to enable workspace managed identity of the datastore.
     1. Go the related datastore and select **Update authentication**.
     :::image type="content" source="../media/resource-known-issues/datastore-update-credential.png" alt-text="Screenshot that shows how to update credentials.":::
     1. Select the toggle switch to use workspace managed identity.
@@ -81,7 +81,7 @@ For detailed information on how to use the Import Data component, see the [Impor
 
 ## Supported sources
 
-This section lists the data sources supported by the designer. Data comes into the designer from either a datastore or from [tabular dataset](how-to-create-register-datasets.md#dataset-types).
+This section lists the data sources supported by the designer. Data comes into the designer from either a datastore or from a [tabular dataset](how-to-create-register-datasets.md#dataset-types).
 
 ### Datastore sources
 

articles/machine-learning/v1/how-to-track-designer-experiments.md

@@ -17,7 +17,7 @@ ms.custom: UpdateFrequency5, designer, sdkv1, devx-track-python
 
 [!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
-In this article, you learn how to add logging code to designer pipelines. You also learn how to view those logs using the Azure Machine Learning studio web portal.
+In this article, you learn how to add code to designer pipelines to enable log metrics. You also learn how to view those logs using the Azure Machine Learning studio web portal.
 
 For more information on logging metrics using the SDK authoring experience, see [Log & view metrics and log files](../how-to-log-view-metrics.md).