Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into rolyon-aadroles-custom-roles-graph-links

Author: rolyon

articles/machine-learning/v1/concept-automated-ml-v1.md

@@ -43,7 +43,7 @@ The following settings allow you to configure your automated ML experiment.
 |----|:----:|:----:|
 |**Split data into train/validation sets**| ✓|✓
 |**Supports ML tasks: classification, regression, & forecasting**| ✓| ✓
-|**Supports computer vision tasks (preview): image classification, object detection & instance segmentation**| ✓| 
+|**Supports computer vision tasks: image classification, object detection & instance segmentation**| ✓| 
 |**Optimizes based on primary metric**| ✓| ✓
 |**Supports Azure ML compute as compute target** | ✓|✓
 |**Configure forecast horizon, target lags & rolling window**|✓|✓
@@ -124,10 +124,7 @@ Advanced forecasting configuration includes:
 
 See examples of regression and automated machine learning for predictions in these Python notebooks: [Sales Forecasting](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb), [Demand Forecasting](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb), and [Forecasting GitHub's Daily Active Users](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb).
 
-### Computer vision (preview)
-
-> [!IMPORTANT]
-> This feature is currently in public preview. This preview version is provided without a service-level agreement. Certain features might not be supported or might have constrained capabilities. For more information, see [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).
+### Computer vision
 
 Support for computer vision tasks allows you to easily generate models trained on image data for scenarios like image classification and object detection. 
 
@@ -156,9 +153,7 @@ Instance segmentation | Tasks to identify objects in an image at the pixel level
 
 <a name="nlp"></a>
 
-### Natural language processing: NLP (preview)
-
-[!INCLUDE [preview disclaimer](../../../includes/machine-learning-preview-generic-disclaimer.md)]
+### Natural language processing: NLP
 
 Support for natural language processing (NLP) tasks in automated ML allows you to easily generate models trained on text data for text classification and named entity recognition scenarios. Authoring automated ML trained NLP models is supported via the Azure Machine Learning Python SDK. The resulting experimentation jobs, models, and outputs can be accessed from the Azure Machine Learning studio UI.
 
@@ -178,7 +173,7 @@ During training, Azure Machine Learning creates a number of pipelines in paralle
 
 Using **Azure Machine Learning**, you can design and run your automated ML training experiments with these steps:
 
-1. **Identify the ML problem** to be solved: classification, forecasting, regression or computer vision (preview).
+1. **Identify the ML problem** to be solved: classification, forecasting, regression or computer vision.
 
 1. **Choose whether you want to use the Python SDK or the studio web experience**:
    Learn about the parity between the [Python SDK and studio web experience](#parity).
@@ -318,7 +313,7 @@ Tutorials are end-to-end introductory examples of AutoML scenarios.
 
 + **For a low or no-code experience**, see the [Tutorial: Train a classification model with no-code AutoML in Azure Machine Learning studio](../tutorial-first-experiment-automated-ml.md).
 
-+ **For using AutoML to train computer vision models**, see the [Tutorial: Train an object detection model (preview) with AutoML and Python (v1)](./tutorial-auto-train-image-models-v1.md).
++ **For using AutoML to train computer vision models**, see the [Tutorial: Train an object detection model with AutoML and Python (v1)](./tutorial-auto-train-image-models-v1.md).
 
 How-to articles provide additional detail into what functionality automated ML offers. For example, 
 

articles/static-web-apps/diagnostics-overview.md

@@ -0,0 +1,46 @@
+---
+title: Diagnose and solve tool for Static Web Apps
+description: Learn to troubleshoot issues with your static web app with the diagnose and solve tool in the Azure portal.
+ms.date: 12/08/2022
+author: craigshoemaker
+ms.service: static-web-apps
+ms.topic:  conceptual
+ms.author: cshoe
+---
+
+# Azure Static Web Apps diagnostics overview
+
+If you encounter issues with your Azure Static Web Apps instance,  the diagnose and solve feature can guide you through steps to troubleshoot problems.
+
+Diagnostics for your static web app are accessible directly from the Azure portal, with no configuration required.
+
+Although these diagnostics are most helpful for issues that have occurred in the last 24 hours, all the diagnostic data remains available for analysis.
+
+## Categories
+
+You have access to diagnostic data in these categories:
+
+| Category | Description | Examples |
+|--|--|--|
+| Availability and performance | Health and performance data | Service uptime, site hits, platform health |
+| Configuration and Management | Application configuration data | Configuration of Static Web Apps features, custom authentication information |
+| Content Deployment | Content deployment data | Deployments |
+
+## View diagnostics
+
+1. From the [Azure portal](https://portal.azure.com), go to your static web app.
+
+1. Select **Diagnose and solve problems**.
+
+From the diagnostics window you can filter diagnostic categories, or select one from the list.
+
+## Reports
+
+Selecting a detector reveals a series of visualization for the diagnostic data. The following screenshot is an example of the availability and performance report.
+
+:::image type="content" source="media/diagnotics-overview/azure-static-web-apps-diagnostics-chart.png" alt-text="Screenshot of Azure Static Web Apps diagnostics chart.":::
+
+## Next steps
+
+> [!div class="nextstepaction"]
+> [Troubleshooting deployment and runtime errors](troubleshooting.md)