Merge pull request #238666 from MicrosoftDocs/main

[ASK MODE] Manual Publish for Azure Monitor and Azure Health

Author: SyntaxC4

articles/azure-monitor/app/opentelemetry-configuration.md

@@ -569,8 +569,8 @@ You might want to enable the OpenTelemetry Protocol (OTLP) Exporter alongside th
     var builder = WebApplication.CreateBuilder(args);
 
     builder.Services.AddOpenTelemetry().UseAzureMonitor();
-    builder.Services.ConfigureOpenTelemetryTracerProvider((sp, builder) => builder.AddOtlpExporter());
-    builder.Services.ConfigureOpenTelemetryMeterProvider((sp, builder) => builder.AddOtlpExporter());
+    builder.Services.AddOpenTelemetry().WithTracing(builder => builder.AddOtlpExporter());
+    builder.Services.AddOpenTelemetry().WithMetrics(builder => builder.AddOtlpExporter());
 
     var app = builder.Build();
 

articles/healthcare-apis/azure-api-for-fhir/release-notes.md

@@ -18,6 +18,13 @@ Azure API for FHIR provides a fully managed deployment of the Microsoft FHIR Ser
 > [!Note]
 > Azure Health Data services is the evolved version of Azure API for FHIR enabling customers to manage FHIR, DICOM, and MedTech services with integrations into other Azure Services. To learn about Azure Health Data Services [click here](https://azure.microsoft.com/products/health-data-services/).
 
+## **May 2023**
+
+**SMART on FHIR : Fixed clinical scope mapping for applications**
+
+This bug fix addresses issue with clinical scope not interpreted correctly for backend applications. 
+For more details, visit [#3250](https://github.com/microsoft/fhir-server/pull/3250)
+
 ## **April 2023**
 
 **Fixed transient issues associated with loading custom search parameters**

articles/healthcare-apis/release-notes.md

@@ -19,6 +19,20 @@ ms.custom: references_regions
 
 Azure Health Data Services is a set of managed API services based on open standards and frameworks for the healthcare industry. They enable you to build scalable and secure healthcare solutions by bringing protected health information (PHI) datasets together and connecting them end-to-end with tools for machine learning, analytics, and AI. This document provides details about the features and enhancements made to Azure Health Data Services including the different service types (FHIR service, DICOM service, and MedTech service) that seamlessly work with one another.
 
+## May 2023
+#### Azure Health Data Services
+
+#### FHIR Service 
+
+**SMART on FHIR : Fixed clinical scope mapping for applications**
+
+This bug fix addresses issue with clinical scope not interpreted correctly for backend applications. 
+For more details, visit [#3250](https://github.com/microsoft/fhir-server/pull/3250)
+
+**Addresses duplicate key error when passed in request parameters and body**
+
+This bug fix handles the issue, when using the POST {resourcetype}/search endpoint to query FHIR resources, the server returns 415 Unsupported Media Type. This is due to repeating a query parameter in the URL query string and the request body. This fix considers all the query parameters from request and body as input. For more details, visit [#3232](https://github.com/microsoft/fhir-server/pull/3232)
+
 ## April 2023
 #### Azure Health Data Services
 

articles/machine-learning/how-to-image-processing-batch.md

@@ -66,7 +66,7 @@ __endpoint.yml__
 
 Run the following code to create the endpoint.
 
-:::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="create_batch_endpoint" :::
+:::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="create_endpoint" :::
 
 # [Python](#tab/python)
 
@@ -193,7 +193,7 @@ One the scoring script is created, it's time to create a batch deployment for it
 
    Then, create the deployment with the following command:
 
-   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="create_batch_deployment_set_default" :::
+   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="create_deployment" :::
 
    # [Python](#tab/python)
 
@@ -342,7 +342,7 @@ For testing our endpoint, we are going to use a sample of 1000 images from the o
 
    To download the predictions, use the following command:
 
-   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="download_scores" :::
+   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="download_outputs" :::
 
    # [Python](#tab/python)
 
@@ -398,7 +398,7 @@ On those cases, we may want to perform inference on the entire batch of data. Th
 
    Then, create the deployment with the following command:
 
-   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="create_batch_deployment_ht" :::
+   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh" ID="create_deployment_ht" :::
 
    # [Python](#tab/python)
 

articles/machine-learning/how-to-nlp-processing-batch.md

@@ -125,7 +125,7 @@ We are going to create a batch endpoint named `text-summarization-batch` where t
 
    # [Azure CLI](#tab/cli)
 
-   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/huggingface-text-summarization/deploy-and-run.sh" ID="create_batch_endpoint" :::
+   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/huggingface-text-summarization/deploy-and-run.sh" ID="create_endpoint" :::
 
    # [Python](#tab/python)
 
@@ -222,7 +222,7 @@ Let's create the deployment that will host the model:
 
    Then, create the deployment with the following command:
 
-   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/huggingface-text-summarization/deploy-and-run.sh" ID="create_batch_deployment_set_default" :::
+   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/huggingface-text-summarization/deploy-and-run.sh" ID="create_deployment" :::
 
    # [Python](#tab/python)
 

articles/machine-learning/how-to-use-batch-endpoints.md

@@ -97,7 +97,7 @@ A batch endpoint is an HTTPS endpoint that clients can call to trigger a batch i
     
     Run the following code to create a batch deployment under the batch endpoint and set it as the default deployment.
 
-    :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="create_batch_endpoint" :::
+    :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="create_endpoint" :::
 
     # [Python](#tab/python)
     
@@ -176,7 +176,7 @@ For instance, the following example downloads the output __score__ from the job.
 
 # [Azure CLI](#tab/cli)
 
-:::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="download_scores" :::
+:::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="download_outputs" :::
 
 # [Python](#tab/python)
 
@@ -212,7 +212,7 @@ To add a new deployment to an existing endpoint, use the  code:
 
 # [Azure CLI](#tab/cli)
 
-:::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="create_new_deployment_not_default" :::
+:::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/mnist-classifier/deploy-and-run.sh" ID="create_deployment_non_default" :::
 
 # [Python](#tab/python)