Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into heidist-work

HeidiSteen · HeidiSteen · commit 2e2338867466 · 2023-05-30T16:27:06.000-07:00
diff --git a/articles/azure-monitor/logs/daily-cap.md b/articles/azure-monitor/logs/daily-cap.md
@@ -69,6 +69,7 @@ Until September 18, 2023, some data security-related data types collected [Micro
 - Update
 - UpdateSummary 
 - CommonSecurityLog
+- Syslog
 
 ## Set the daily cap
 ### Log Analytics workspace
diff --git a/articles/machine-learning/how-to-authenticate-batch-endpoint.md b/articles/machine-learning/how-to-authenticate-batch-endpoint.md
@@ -91,7 +91,40 @@ In this case, we want to execute a batch endpoint using the identity of the user
 
 # [REST](#tab/rest)
 
-When working with REST APIs, we recommend to using either a [service principal](#running-jobs-using-a-service-principal) or a [managed identity](#running-jobs-using-a-managed-identity) to interact with the API.
+When working with REST, we recommend invoking batch endpoints using a service principal. However, if you want to test a particular deployment using REST with your own credentials, you can do it by generating an Azure AD token for your account. Follow these steps:
+
+1. The simplest way to get a valid token for your user account is to use the Azure CLI. In a console, run the following command:
+
+    ```azurecli
+    az account get-access-token --resource https://ml.azure.com --query "accessToken" --output tsv
+    ```
+    
+1. Take note of the generated output.
+
+1. Once authenticated, make a request to the invocation URI replacing `<TOKEN>` by the one you obtained before.
+    
+    __Request__:
+    
+    ```http
+    POST jobs HTTP/1.1
+    Host: <ENDPOINT_URI>
+    Authorization: Bearer <TOKEN>
+    Content-Type: application/json
+    ```
+    __Body:__
+        
+    ```json
+    {
+        "properties": {
+    	    "InputData": {
+    		"mnistinput": {
+    		    "JobInputType" : "UriFolder",
+    		    "Uri":  "https://azuremlexampledata.blob.core.windows.net/data/heart-disease-uci"
+    	        }
+            }
+        }
+    }
+    ```
 
 ---
 
diff --git a/articles/machine-learning/how-to-deploy-model-custom-output.md b/articles/machine-learning/how-to-deploy-model-custom-output.md
@@ -154,7 +154,7 @@ Follow the next steps to create a deployment using the previous scoring script:
 
    # [Azure CLI](#tab/cli)
    
-   To create a new deployment under the created endpoint, create a `YAML` configuration like the following:
+   To create a new deployment under the created endpoint, create a `YAML` configuration like the following. You can check the [full batch endpoint YAML schema](reference-yaml-endpoint-batch.md) for extra properties.
    
    :::code language="yaml" source="~/azureml-examples-batch-pup/cli/endpoints/batch/deploy-models/custom-outputs-parquet/deployment.yml":::
    
diff --git a/articles/machine-learning/how-to-image-processing-batch.md b/articles/machine-learning/how-to-image-processing-batch.md
@@ -187,7 +187,7 @@ One the scoring script is created, it's time to create a batch deployment for it
 
    # [Azure CLI](#tab/cli)
    
-   To create a new deployment under the created endpoint, create a `YAML` configuration like the following:
+   To create a new deployment under the created endpoint, create a `YAML` configuration like the following. You can check the [full batch endpoint YAML schema](reference-yaml-endpoint-batch.md) for extra properties.
    
    :::code language="yaml" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deployment-by-file.yml":::
   
@@ -392,7 +392,7 @@ On those cases, we may want to perform inference on the entire batch of data. Th
 
    # [Azure CLI](#tab/cli)
    
-   To create a new deployment under the created endpoint, create a `YAML` configuration like the following:
+   To create a new deployment under the created endpoint, create a `YAML` configuration like the following. You can check the [full batch endpoint YAML schema](reference-yaml-endpoint-batch.md) for extra properties.
    
    :::code language="yaml" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deployment-by-batch.yml":::
   
diff --git a/articles/machine-learning/how-to-mlflow-batch.md b/articles/machine-learning/how-to-mlflow-batch.md
@@ -115,7 +115,7 @@ Follow these steps to deploy an MLflow model to a batch endpoint for running bat
 
     # [Azure CLI](#tab/cli)
    
-    To create a new deployment under the created endpoint, create a `YAML` configuration like the following:
+    To create a new deployment under the created endpoint, create a `YAML` configuration like the following. You can check the [full batch endpoint YAML schema](reference-yaml-endpoint-batch.md) for extra properties.
    
     :::code language="yaml" source="~/azureml-examples-batch-pup/cli/endpoints/batch/deploy-models/heart-classifier-mlflow/deployment-simple/deployment.yml" :::
    
@@ -363,7 +363,7 @@ Use the following steps to deploy an MLflow model with a custom scoring script.
 
     # [Azure CLI](#tab/cli)
    
-    To create a new deployment under the created endpoint, create a `YAML` configuration like the following:
+    To create a new deployment under the created endpoint, create a `YAML` configuration like the following. You can check the [full batch endpoint YAML schema](reference-yaml-endpoint-batch.md) for extra properties.
    
     :::code language="yaml" source="~/azureml-examples-batch-pup/cli/endpoints/batch/deploy-models/heart-classifier-mlflow/deployment-custom/deployment.yml" :::
    
diff --git a/articles/machine-learning/how-to-nlp-processing-batch.md b/articles/machine-learning/how-to-nlp-processing-batch.md
@@ -214,7 +214,7 @@ Let's create the deployment that hosts the model:
 
    # [Azure CLI](#tab/cli)
    
-   To create a new deployment under the created endpoint, create a `YAML` configuration like the following:
+   To create a new deployment under the created endpoint, create a `YAML` configuration like the following. You can check the [full batch endpoint YAML schema](reference-yaml-endpoint-batch.md) for extra properties.
    
    __deployment.yml__
    
diff --git a/articles/machine-learning/how-to-use-batch-pipeline-deployments.md b/articles/machine-learning/how-to-use-batch-pipeline-deployments.md
@@ -135,7 +135,7 @@ To deploy the pipeline component, we have to create a batch deployment. A deploy
 
     # [Azure CLI](#tab/cli)
     
-    The `deployment.yml` file contains the deployment's configuration.
+    The `deployment.yml` file contains the deployment's configuration. You can check the [full batch endpoint YAML schema](reference-yaml-endpoint-batch.md) for extra properties.
 
     __deployment.yml__
 
diff --git a/articles/machine-learning/how-to-use-batch-scoring-pipeline.md b/articles/machine-learning/how-to-use-batch-scoring-pipeline.md
@@ -273,7 +273,7 @@ To deploy the pipeline component, we have to create a batch deployment. A deploy
 
     # [Azure CLI](#tab/cli)
     
-    The `deployment.yml` file contains the deployment's configuration.
+    The `deployment.yml` file contains the deployment's configuration. You can check the [full batch endpoint YAML schema](reference-yaml-endpoint-batch.md) for extra properties.
 
     __deployment.yml__
 
@@ -431,4 +431,4 @@ ml_client.compute.begin_delete(name="batch-cluster")
 
 - [Create batch endpoints from pipeline jobs (preview)](how-to-use-batch-pipeline-from-job.md)
 - [Accessing data from batch endpoints jobs](how-to-access-data-batch-endpoints-jobs.md)
-- [Troubleshooting batch endpoints](how-to-troubleshoot-batch-endpoints.md)
+- [Troubleshooting batch endpoints](how-to-troubleshoot-batch-endpoints.md)
diff --git a/articles/machine-learning/how-to-use-batch-training-pipeline.md b/articles/machine-learning/how-to-use-batch-training-pipeline.md
@@ -257,7 +257,7 @@ To deploy the pipeline component, we have to create a batch deployment. A deploy
 
     # [Azure CLI](#tab/cli)
     
-    The `deployment-ordinal/deployment.yml` file contains the deployment's configuration.
+    The `deployment-ordinal/deployment.yml` file contains the deployment's configuration. You can check the [full batch endpoint YAML schema](reference-yaml-endpoint-batch.md) for extra properties.
 
     __deployment-ordinal/deployment.yml__
 
@@ -401,7 +401,7 @@ By default, we used `ordinal` previously. Let's now change the categorical encod
 
     # [Azure CLI](#tab/cli)
     
-    The `deployment-onehot/deployment.yml` file contains the deployment's configuration.
+    The `deployment-onehot/deployment.yml` file contains the deployment's configuration. You can check the [full batch endpoint YAML schema](reference-yaml-endpoint-batch.md) for extra properties.
 
     __deployment-onehot/deployment.yml__
 
@@ -527,4 +527,4 @@ ml_client.compute.begin_delete(name="batch-cluster")
 - [How to deploy a pipeline to perform batch scoring with preprocessing (preview)](how-to-use-batch-scoring-pipeline.md)
 - [Create batch endpoints from pipeline jobs (preview)](how-to-use-batch-pipeline-from-job.md)
 - [Accessing data from batch endpoints jobs](how-to-access-data-batch-endpoints-jobs.md)
-- [Troubleshooting batch endpoints](how-to-troubleshoot-batch-endpoints.md)
+- [Troubleshooting batch endpoints](how-to-troubleshoot-batch-endpoints.md)
