Merge branch 'main' into release-mssecure2024-MDC

Author: v-alje

.openpublishing.redirection.operator-5g-core.json

@@ -0,0 +1,49 @@
+{
+    "redirections": [
+        {
+            "source_path_from_root": "/articles/operator-5g-core/quickstart-configure-network-function.md ",
+            "redirect_url": "/azure/operator-5g-core/how-to-configure-network-function",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/operator-5g-core/quickstart-delete-network-function-cluster.md",
+            "redirect_url": "/azure/operator-5g-core/how-to-delete-network-function-cluster",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/operator-5g-core/quickstart-deploy-observability.md",
+            "redirect_url": "/azure/operator-5g-core/how-to-deploy-observability",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/operator-5g-core/how-to-complete-prerequisites-deploy-azure-kubernetes-service.md",
+            "redirect_url": "/azure/operator-5g-core/quickstart-complete-prerequisites-deploy-azure-kubernetes-service",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/operator-5g-core/how-to-complete-prerequisites-deploy-nexus-azure-kubernetes-service.md",
+            "redirect_url": "/azure/operator-5g-core/quickstart-complete-prerequisites-deploy-nexus-azure-kubernetes-service",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/operator-5g-core/how-to-deploy-5g-core.md",
+            "redirect_url": "/azure/operator-5g-core/quickstart-deploy-5g-core",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/operator-5g-core/how-to-monitor-deployment-status.md",
+            "redirect_url": "/azure/operator-5g-core/quickstart-monitor-deployment-status",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/operator-5g-core/how-to-perform-checks-post-deployment.md",
+            "redirect_url": "/azure/operator-5g-core/quickstart-perform-checks-post-deployment",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/operator-5g-core/quickstart-deploy-network-functions.md",
+            "redirect_url": "/azure/operator-5g-core/how-to-deploy-network-functions",
+            "redirect_document_id": false
+        }
+   ]
+}

articles/advisor/advisor-high-availability-recommendations.md

@@ -12,7 +12,7 @@ Azure Advisor helps you ensure and improve the continuity of your business-criti
 
 ## Check the version of your Check Point network virtual appliance image
 
-Advisor can identify whether your virtual machine is running a version of the Check Point image that has been known to lose network connectivity during platform servicing operations. The Advisor recommendation will help you upgrade to a newer version of the image that addresses this problem. This check will ensure business continuity through better network connectivity.
+Advisor can identify whether your virtual machine is running a version of the Check Point image that has been known to lose network connectivity during platform servicing operations. The Advisor recommendation helps you upgrade to a newer version of the image that addresses this problem. This check ensures business continuity through better network connectivity.
 
 ## Ensure application gateway fault tolerance
 
@@ -56,7 +56,7 @@ Azure Advisor checks for any VPN gateways that use a Basic SKU and recommends th
 - Higher stability and availability.
 
 ## Ensure reliable outbound connectivity with VNet NAT
-Using default outbound connecitivty provided by a Standard Load Balancer or other Azure resources is not recommended for production workloads as this causes connection failures (also called SNAT port exhaustion). The recommended approach is using a VNet NAT which will prevent any failures of connectivity in this regard. NAT can scale seamlessly to ensure your application is never out ports. [Learn more about VNet NAT](../virtual-network/nat-gateway/nat-overview.md).
+Using default outbound connectivity provided by a Standard Load Balancer or other Azure resources is not recommended for production workloads as this causes connection failures (also called SNAT port exhaustion). The recommended approach is using a VNet NAT which will prevent any failures of connectivity in this regard. NAT can scale seamlessly to ensure your application is never out ports. [Learn more about VNet NAT](../virtual-network/nat-gateway/nat-overview.md).
 
 ## Ensure virtual machine fault tolerance (temporarily disabled)
 
@@ -76,7 +76,7 @@ Virtual machines in an availability set with disks that share either storage acc
 ## Repair invalid log alert rules
 
 Azure Advisor detects log alert rules that have invalid queries specified in their condition section. 
-Azure Monitor log alert rules run queries at specified frequency and fire alerts based on the results. Queries can become invalid over time because of changes in the referenced resources, tables, or commands. Advisor recommends corrections for alert queries to prevent the rules from being automatically disabled and to ensure monitoring coverage. For more information, see [Troubleshooting alert rules](../azure-monitor/alerts/alerts-troubleshoot-log.md#query-used-in-a-log-alert-isnt-valid)
+Azure Monitor log alert rules run queries at specified frequency and fire alerts based on the results. Queries can become invalid over time because of changes in the referenced resources, tables, or commands. Advisor recommends corrections for alert queries to prevent the rules from being automatically disabled and to ensure monitoring coverage. For more information, see [Troubleshooting alert rules](../azure-monitor/alerts/alerts-troubleshoot-log.md)
 
 ## Configure Consistent indexing mode on your Azure Cosmos DB collection
 
@@ -110,10 +110,10 @@ Starting July 1, 2020, you won't be able to create new Spark clusters by using S
 Virtual machines that don't have replication enabled to another region aren't resilient to regional outages. Replicating virtual machines reduces any adverse business impact during Azure region outages. Advisor detects VMs on which replication isn't enabled and recommends enabling it. When you enable replication, if there's an outage, you can quickly bring up your virtual machines in a remote Azure region. [Learn more about virtual machine replication.](../site-recovery/azure-to-azure-quickstart.md)
 
 ## Upgrade to the latest version of the Azure Connected Machine agent
-The [Azure Connected Machine agent](../azure-arc/servers/manage-agent.md) is updated regularly with bug fixes, stability enhancements, and new functionality. We have identified resources which are not working on the latest version of machine agent and this Advisor recommendation will suggest you to upgrade your agent to the latest version for the best Azure Arc experience.
+The [Azure Connected Machine agent](../azure-arc/servers/manage-agent.md) is updated regularly with bug fixes, stability enhancements, and new functionality. We have identified resources which are not working on the latest version of machine agent and this Advisor recommendation suggests that you to upgrade your agent to the latest version for the best Azure Arc experience.
 
 ## Do not override hostname to ensure website integrity
-Advisor recommend to try avoid overriding the hostname when configuring Application Gateway. Having a different domain on the frontend of Application Gateway than the one which is used to access the backend can potentially lead to cookies or redirect URLs being broken. Note that this might not be the case in all situations and that certain categories of backends (like REST API's) in general are less sensitive to this. Please make sure the backend is able to deal with this or update the Application Gateway configuration so the hostname does not need to be overwritten towards the backend. When used with App Service, attach a custom domain name to the Web App and avoid use of the `*.azurewebsites.net` host name towards the backend. [Learn more about custom domain](../application-gateway/troubleshoot-app-service-redirection-app-service-url.md).
+Advisor recommends that you try avoid overriding the hostname when configuring Application Gateway. Having a different domain on the frontend of Application Gateway than the one which is used to access the backend can potentially lead to cookies or redirect URLs being broken. Note that this might not be the case in all situations and that certain categories of backends (like REST APIs) in general are less sensitive to this. Please make sure the backend is able to deal with this or update the Application Gateway configuration so the hostname does not need to be overwritten towards the backend. When used with App Service, attach a custom domain name to the Web App and avoid use of the `*.azurewebsites.net` host name towards the backend. [Learn more about custom domain](../application-gateway/troubleshoot-app-service-redirection-app-service-url.md).
 
 ## Next steps
 

articles/ai-services/TOC.yml

@@ -31,8 +31,8 @@
     href: cognitive-services-custom-subdomains.md
 - name: How-to
   items:
-  - name: Plan and manage costs 
-    href: plan-manage-costs.md
+  - name: Plan and manage costs
+    href: ../ai-studio/how-to/costs-plan-manage.md?context=/azure/ai-services/context/context
   - name: Increase rate limit
     href: autoscale.md
   - name: Use commitment tier pricing 

articles/ai-services/document-intelligence/concept-accuracy-confidence.md

@@ -18,11 +18,11 @@ ms.author: lajanuar
 
 > [!NOTE]
 >
-> * **Custom neural models do not provide accuracy scores during training**.
-> * Confidence scores for structured fields such as tables are currently unavailable.
+> * **Custom neural models** do not provide accuracy scores during training.
+> * Confidence scores for tables, table rows and table cells are available starting with the **2024-02-29-preview** API version for **custom models**.
 
 
-Custom models generate an estimated accuracy score when trained. Documents analyzed with a custom model produce a confidence score for extracted fields. In this article, learn to interpret accuracy and confidence scores and best practices for using those scores to improve accuracy and confidence results.
+Custom template models generate an estimated accuracy score when trained. Documents analyzed with a custom model produce a confidence score for extracted fields. In this article, learn to interpret accuracy and confidence scores and best practices for using those scores to improve accuracy and confidence results.
 
 ## Accuracy scores
 
@@ -38,21 +38,25 @@ The accuracy value range is a percentage between 0% (low) and 100% (high). The e
 
 > [!NOTE]
 >
-> * **Table cell confidence scores are now included with the 2024-02-29-preview API version**.
+> * **Table, row and cell confidence scores are now included with the 2024-02-29-preview API version**.
 > * Confidence scores for table cells from custom models is added to the API starting with the 2024-02-29-preview API.
 
 Document Intelligence analysis results return an estimated confidence for predicted words, key-value pairs, selection marks, regions, and signatures. Currently, not all document fields return a confidence score.
 
 Field confidence indicates an estimated probability between 0 and 1 that the prediction is correct. For example, a confidence value of 0.95 (95%) indicates that the prediction is likely correct 19 out of 20 times. For scenarios where accuracy is critical, confidence can be used to determine whether to automatically accept the prediction or flag it for human review.
 
-Confidence scores have two data points: the field level confidence score and the text extraction confidence score. In addition to the field confidence of position and span, the text extraction confidence in the ```pages``` section of the response is the model's confidence in the text extraction (OCR) process. The two confidence scores should be combined to generate one overall confidence score.
-
 **Document Intelligence Studio** </br>
 **Analyzed invoice prebuilt-invoice model**
 
 :::image type="content" source="media/accuracy-confidence/confidence-scores.png" alt-text="confidence scores from Document Intelligence Studio":::
 
-## Interpret accuracy and confidence scores
+## Interpret accuracy and confidence scores for custom models
+
+When interpreting the confidence score from a custom model, you should consider all the confidence scores returned from the model. Let's start with a list of all the confidence scores.
+1. **Document type confidence score**: The document type confidence is an indicator of closely the analyzed document resembleds documents in the training dataset. When the document type confidence is low, this is indicative of template or structural variations in the analyzed document. To improve the document type confidence, label a document with that specific variation and add it to your training dataset. Once the model is re-trained, it should be better equipped to handl that class of variations.
+2. **Field level confidence**: Each labled field extracted has an associated confidence score. This score reflects the model's confidence on the position of the value extracted. While evaluating the confidence you should also look at the underlying extraction confidence to generate a comprehensive confidence for the extracted result. Evaluate the OCR results for text extraction or selection marks depending on the field type to generate a composite confidence score for the field.
+3. **Word confidence score** Each word extracted within the document has an associated confidence score. The score represents the confidence of the transcription. The pages array contains an array of words, each word has an associated span and confidence. Spans from the custom field extracted values will match the spans of the extracted words.
+4. **Selection mark confidence score**: The pages array also contains an array of selection marks, each selection mark has a confidence score representing the confidence of the seletion mark and selection state detection. When a labeled field is a selection mark, the custom field selection confidence combined with the selection mark confidence is an accurate representation of the overall confidence that the field was extracted correctly.
 
 The following table demonstrates how to interpret both the accuracy and confidence scores to measure your custom model's performance.
 
@@ -65,7 +69,7 @@ The following table demonstrates how to interpret both the accuracy and confiden
 
 ## Table, row, and cell confidence
 
-With the addition of table, row and cell confidence with the ```2024-02-29-preview``` API, here are some common questions that should help with interpreting the scores:
+With the addition of table, row and cell confidence with the ```2024-02-29-preview``` API, here are some common questions that should help with interpreting the table, row and cell scores:
 
 **Q:** Is it possible to see a high confidence score for cells, but a low confidence score for the row?<br>
 

articles/ai-services/document-intelligence/concept-custom-classifier.md

@@ -66,7 +66,7 @@ With custom models, you need to maintain access to the training dataset to updat
 
 > [!IMPORTANT]
 >
-> Incremental trainiing is only supported with models trained with the same API version. If you are trying to extend a model, use the API version the original model was trained with to extend the model.
+> Incremental training is only supported with models trained with the same API version. If you are trying to extend a model, use the API version the original model was trained with to extend the model. Incremental training is only supported with API version **2024-02-29-preview** or later.
 
 Incremental training requires that you provide the original model ID as the `baseClassifierId`. See [incremental training](concept-incremental-classifier.md) to learn more about how to use incremental training.
 

articles/ai-services/document-intelligence/concept-custom-neural.md

@@ -65,7 +65,24 @@ Neural models support documents that have the same information, but different pa
 
 *See* our [Language Support—custom models](language-support-custom.md) page for a complete list of supported languages.
 
-## Tabular fields
+## Overlapping fields
+
+With the release of API versions **2024-02-29-preview** and  later, custom neural models will support overlapping fields:
+
+To use the overlapping fields, your dataset needs to contain at least one sample with the expected overlap. To label an overlap, use **region labeling** to designate each of the spans of content (with the overlap) for each field. Labeling an overlap with field selection (highlighting a value) will fail in the studio as region labeling is the only supported labeling tool for indicating field overlaps. Overlap support includes:
+
+* Complete overlap. The same set of tokens are labeled for two different fields.
+* Partial overlap. Some tokens belong to both fields, but there are tokens that are only part of one field or the other.
+
+Overlapping fields have some limits:
+
+* Any token or word can only be labeled as two fields.
+* overlapping fields in a table can't span table rows.
+* Overlapping fields can only be recognized if at least one sample in the dataset contains overlapping labels for those fields.
+
+To use overlapping fields, label your dataset with the overlaps and train the model with the API version ```2024-02-29-preview``` or later.
+
+## Tabular fields adds table, row and cell confidence
 
 With the release of API versions **2022-06-30-preview** and  later, custom neural models will support tabular fields (tables):
 
@@ -92,23 +109,6 @@ Tabular fields provide **table, row and cell confidence** starting with the ```2
 
 See  [confidence and accuracy scores](concept-accuracy-confidence.md) to learn more about table, row, and cell confidence.
 
-## Overlapping fields
-
-With the release of API versions **2024-02-29-preview** and  later, custom neural models will support overlapping fields:
-
-To use the overlapping fields, your dataset needs to contain at least one sample with the expected overlap. To label an overlap, use region labeling to designate each of the spans of content (with the overlap) for each field. Overlap support includes:
-
-* Complete overlap. The same set of tokens are labeled for two different fields.
-* Partial overlap. Some tokens belong to both fields, but there are tokens that are only part of one field or the other.
-
-Overlapping fields have some limits:
-
-* Any token or word can only be labeled as two fields.
-* overlapping fields in a table can't span table rows.
-* Overlapping fields can only be recognized if at least one sample in the dataset contains overlapping labels for those fields.
-
-To use overlapping fields, label your dataset with the overlaps and train the model with the API version ```2024-02-29-preview``` or later.
-
 
 ## Supported regions