| Service | Microsoft Docs article | Related commit history on GitHub | Change details |
|---|---|---|---|
| azure-monitor | Opentelemetry Configuration | https://github.com/MicrosoftDocs/azure-docs/commits/main/articles/azure-monitor/app/opentelemetry-configuration.md | 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(); |
| healthcare-apis | Release Notes | https://github.com/MicrosoftDocs/azure-docs/commits/main/articles/healthcare-apis/azure-api-for-fhir/release-notes.md | 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** |
| healthcare-apis | Release Notes | https://github.com/MicrosoftDocs/azure-docs/commits/main/articles/healthcare-apis/release-notes.md | +## 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 |
| machine-learning | How To Image Processing Batch | https://github.com/MicrosoftDocs/azure-docs/commits/main/articles/machine-learning/how-to-image-processing-batch.md | __endpoint.yml__ Run the following code to create the endpoint. # [Python](#tab/python) 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) 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) 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) |
| machine-learning | How To Nlp Processing Batch | https://github.com/MicrosoftDocs/azure-docs/commits/main/articles/machine-learning/how-to-nlp-processing-batch.md | 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) 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) |
| machine-learning | How To Use Batch Endpoints | https://github.com/MicrosoftDocs/azure-docs/commits/main/articles/machine-learning/how-to-use-batch-endpoints.md | 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) For instance, the following example downloads the output __score__ from the job. # [Azure CLI](#tab/cli) # [Python](#tab/python) To add a new deployment to an existing endpoint, use the code: # [Azure CLI](#tab/cli) # [Python](#tab/python) |