Merge branch 'master' of https://github.com/MicrosoftDocs/azure-docs-pr into upOwner

Author: roygara

articles/app-service/overview-managed-identity.md

@@ -306,7 +306,10 @@ The **MSI_ENDPOINT** is a local URL from which your app can request tokens. To g
 > |resource|Query|The AAD resource URI of the resource for which a token should be obtained. This could be one of the [Azure services that support Azure AD authentication](../active-directory/managed-identities-azure-resources/services-support-managed-identities.md#azure-services-that-support-azure-ad-authentication) or any other resource URI.|
 > |api-version|Query|The version of the token API to be used. "2017-09-01" is currently the only version supported.|
 > |secret|Header|The value of the MSI_SECRET environment variable. This header is used to help mitigate server-side request forgery (SSRF) attacks.|
-> |clientid|Query|(Optional) The ID of the user-assigned identity to be used. If omitted, the system-assigned identity is used.|
+> |clientid|Query|(Optional unless for user-assigned) The ID of the user-assigned identity to be used. If omitted, the system-assigned identity is used.|
+
+> [!IMPORTANT]
+> If you are attempting to obtain tokens for user-assigned identities, you must include the `clientid` property. Otherwise the token service will attempt to obtain a token for a system-assigned identity, which may or may not exist.
 
 A successful 200 OK response includes a JSON body with the following properties:
 

articles/cosmos-db/create-graph-gremlin-console.md

@@ -54,7 +54,7 @@ You also need to install the [Gremlin Console](https://tinkerpop.apache.org/). U
     username|*Your username*|The resource of the form `/dbs/<db>/colls/<coll>` where `<db>` is your database name and `<coll>` is your collection name.
     password|*Your primary key*| See second screenshot below. This is your primary key, which you can retrieve from the Keys page of the Azure portal, in the Primary Key box. Use the copy button on the left side of the box to copy the value.
     connectionPool|{enableSsl: true}|Your connection pool setting for SSL.
-    serializer|{ className: org.apache.tinkerpop.gremlin.<br>driver.ser.GraphSONMessageSerializerV1d0,<br> config: { serializeResultToString: true }}|Set to this value and delete any `\n` line breaks when pasting in the value.
+    serializer|{ className: org.apache.tinkerpop.gremlin.<br>driver.ser.GraphSONMessageSerializerV2d0,<br> config: { serializeResultToString: true }}|Set to this value and delete any `\n` line breaks when pasting in the value.
 
     For the hosts value, copy the **Gremlin URI** value from the **Overview** page:
 ![View and copy the Gremlin URI value on the Overview page in the Azure portal](./media/create-graph-gremlin-console/gremlin-uri.png)

articles/cosmos-db/gremlin-support.md

@@ -43,7 +43,7 @@ The following table lists the TinkerPop features that are implemented by Azure C
 
 ## Gremlin wire format: GraphSON
 
-Azure Cosmos DB uses the [GraphSON format](https://tinkerpop.apache.org/docs/3.3.2/reference/#graphson-reader-writer) when returning results from Gremlin operations. GraphSON is the Gremlin standard format for representing vertices, edges, and properties (single and multi-valued properties) using JSON. 
+Azure Cosmos DB uses the [GraphSON format](http://tinkerpop.apache.org/docs/current/reference/#graphson) when returning results from Gremlin operations. Azure Cosmos DB currently supports "GraphSONv2" version. GraphSON is the Gremlin standard format for representing vertices, edges, and properties (single and multi-valued properties) using JSON.
 
 For example, the following snippet shows a GraphSON representation of a vertex *returned to the client* from Azure Cosmos DB. 
 

articles/machine-learning/service/how-to-deploy-custom-docker-image.md

@@ -53,7 +53,7 @@ The information in this section assumes that you are using an Azure Container Re
     When using images stored in the __container registry for the workspace__, you do not need to authenticate to the registry. Authentication is handled by the workspace.
 
     > [!WARNING]
-    > The Azure Container Rzegistry for your workspace is __created the first time you train or deploy a model__ using the workspace. If you've created a new workspace, but not trained or created a model, no Azure Container Registry will exist for the workspace.
+    > The Azure Container Registry for your workspace is __created the first time you train or deploy a model__ using the workspace. If you've created a new workspace, but not trained or created a model, no Azure Container Registry will exist for the workspace.
 
     For information on retrieving the name of the Azure Container Registry for your workspace, see the [Get container registry name](#getname) section of this article.
 

articles/machine-learning/service/tutorial-first-experiment-automated-ml.md

@@ -9,7 +9,7 @@ ms.topic: tutorial
 ms.author: tzvikei
 author: tsikiksr
 ms.reviewer: nibaccam
-ms.date: 09/09/2019
+ms.date: 09/26/2019
 
 # Customer intent: As a non-coding data scientist, I want to use automated machine learning techniques so that I can build a classification model.
 ---
@@ -55,10 +55,11 @@ You complete the following experiment set-up and run steps in the workspace land
 
 1. Select **Get started**.
 
-1.  Select **Automated ML** under the **Authoring** section, on the left side pane.
-You'll see the **Getting started** screen, since this is your first experiment with Automated Machine Learning.
+1. In the left pane, select **Automated ML** under the **Authoring** section.
 
-    ![Azure Machine Learning studio](media/tutorial-1st-experiment-automated-ml/get-started.png)
+   Since this is your first automated ML experiment, you'll see the Getting started screen.
+
+   ![Azure Machine Learning studio](media/tutorial-1st-experiment-automated-ml/get-started.png)
 
 1. Select **Create experiment**. 
 
@@ -76,6 +77,7 @@ You'll see the **Getting started** screen, since this is your first experiment w
    >For this tutorial, you'll use the default storage account and container created with your new compute. They automatically populate in the form.
     
 1. Select **Create** to get the compute target. 
+
    **This takes a couple minutes to complete.** 
 
 1. After creation, select your new compute target from the drop-down list and select **Next**.
@@ -89,18 +91,18 @@ You'll see the **Getting started** screen, since this is your first experiment w
     1. Give your dataset a unique name and provide an optional description. 
 
     1. Select **Next** on the bottom left,  to  upload it to the default container that was automatically set up during your workspace creation. Public preview supports only local file uploads. 
-
-    1. When the upload is complete, the **Settings and preview** form is intelligently populated based on the file type. Ensure the form is populated as follows.
+    
+       When the upload is complete, the Settings and preview form is pre-populated based on the file type. 
+       
+    1. Verify that the **Settings and preview** form is populated as follows and select **Next**.
 
-        Field|Value
+        Field|Value for tutorial
         ---|---
         File format| Delimited
         Delimiter| Comma
         Encoding| UTF-8
         Column headers| All files have same headers
         Skip rows | None
-
-        Select **Next**.
 
     1. The **Schema** form allows for further configuration of your data for this experiment. For this example, select the toggle switch for the **day_of_week** feature, so as to not include it for this experiment. Select **Done**, to complete the file upload and creation of the dataset for your experiment.
 
@@ -113,16 +115,17 @@ You'll see the **Getting started** screen, since this is your first experiment w
 1. Expand **Advanced Settings** and populate the fields as follows.
 
    >[!NOTE]
-   > For this experiment, you don't set a metric score or max cores per iterations threshold. You also don't block algorithms from being tested.
+   > In this tutorial, you won't set a metric score or max cores per iterations threshold. Nor will you block algorithms from being tested.
+   
+   Advanced settings|Description|Value for tutorial
+   ------|---------|---
+   Primary metric| Evaluation metric that the machine learning algorithm will be measured by.|AUC_weighted
+   Exit criteria| If a criteria is met, the training job is stopped. |Training&nbsp;job&nbsp;time: 5 <br> <br> Max&nbsp;#&nbsp;of&nbsp;iterations&#58;10
+   Preprocessing| Enables preprocessing done by automated machine learning. This includes automatic data cleansing, preparing, and transformation to generate synthetic features.| Enable
+   Validation type | Choose a cross-validation type.|K-fold cross-validation
+   Number of validations | Number of tests. | 2 cross-validations 
+   Concurrency| The number of max concurrent iterations.|5
 
-    Advanced&nbsp;settings|Description|Value&nbsp;for&nbsp;tutorial
-    ------|---------|---
-    Primary metric| Evaluation metric that the machine learning algorithm will be measured by.|**AUC_weighted** 
-    Exit criteria| When any of these criteria are met, the training job ends even if it didn't fully complete. |Training&nbsp;job&nbsp;time&nbsp;(minutes): **5**  <br> <br> Max&nbsp;#&nbsp;of&nbsp;iterations&#58;**10** 
-    Preprocessing| Enables preprocessing done by automated machine learning. This includes automatic data cleansing, preparing, and transformation to generate synthetic features.| Enable
-    Validation| Validation type and number of tests. | **K-fold** cross-validation<br><br>  cross-validations: **2** 
-    Concurrency| The number of max concurrent iterations.|**5**
-
 1. Select **Start** to run the experiment. A screen appears with a status message as the experiment preparation begins.
 
 >[!IMPORTANT]