july freshness

Author: laujan

.openpublishing.redirection.json

@@ -1242,7 +1242,22 @@
         },
         {
             "source_path_from_root": "/articles/azure-maps/tutorial-creator-feature-stateset.md",
-            "redirect_url": "/azure/azure-maps/how-to-creator-feature-stateset",
+            "redirect_url": "/azure/azure-maps/about-creator",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/azure-maps/schema-stateset-stylesobject.md",
+            "redirect_url": "/azure/azure-maps/about-creator",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/azure-maps/indoor-map-dynamic-styling.md",
+            "redirect_url": "/azure/azure-maps/about-creator",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/azure-maps/how-to-creator-feature-stateset.md",
+            "redirect_url": "/azure/azure-maps/about-creator",
             "redirect_document_id": false
         },
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@@ -2125,6 +2140,11 @@
             "redirect_url": "/azure/devops/pipelines/get-started-designer?toc=%2Fazure%2Fdevops%2Fuser-guide%2Ftoc.json&bc=%2Fazure%2Fdevops%2Fuser-guide%2Fbreadcrumb%2Ftoc.json&tabs=new-nav",
             "redirect_document_id": false
         },
+        {
+            "source_path_from_root": "/articles/devtest/index.md",
+            "redirect_url": "/azure/devtest/offer/",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/devtest-labs/devtest-lab-add-artifact-repo.md",
             "redirect_url": "/azure/devtest-labs/add-artifact-repository",
@@ -4497,6 +4517,11 @@
             "source_path_from_root": "/articles/kubernetes-fleet/configuration-propagation.md",
             "redirect_url": "/azure/kubernetes-fleet/resource-propagation"
         },
+        {
+            "source_path_from_root": "/articles/aks/network-observability-byo-cli.md",
+            "redirect_url": "/azure/aks/network-observability-overview",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/orbital/license-spacecraft.md",
             "redirect_url": "/azure/orbital/initiate-licensing",

articles/advisor/advisor-overview.md

@@ -2,7 +2,7 @@
 title: Introduction to Azure Advisor
 description: Use Azure Advisor to optimize your Azure deployments.
 ms.topic: overview
-ms.date: 04/07/2022
+ms.date: 07/08/2024
 ---
 
 # Introduction to Azure Advisor
@@ -22,11 +22,11 @@ You can access Advisor through the [Azure portal](https://aka.ms/azureadvisordas
 
 The Advisor dashboard displays personalized recommendations for all your subscriptions. The recommendations are divided into five categories: 
 
-* **Reliability**: To ensure and improve the continuity of your business-critical applications. For more information, see [Advisor Reliability recommendations](advisor-high-availability-recommendations.md).
+* **Reliability**: To ensure and improve the continuity of your business-critical applications. For more information, see [Advisor Reliability recommendations](advisor-reference-reliability-recommendations.md).
 * **Security**: To detect threats and vulnerabilities that might lead to security breaches. For more information, see [Advisor Security recommendations](advisor-security-recommendations.md).
-* **Performance**: To improve the speed of your applications. For more information, see [Advisor Performance recommendations](advisor-performance-recommendations.md).
-* **Cost**: To optimize and reduce your overall Azure spending. For more information, see [Advisor Cost recommendations](advisor-cost-recommendations.md).
-* **Operational Excellence**: To help you achieve process and workflow efficiency, resource manageability and deployment best practices. For more information, see [Advisor Operational Excellence recommendations](advisor-operational-excellence-recommendations.md).
+* **Performance**: To improve the speed of your applications. For more information, see [Advisor Performance recommendations](advisor-reference-performance-recommendations.md).
+* **Cost**: To optimize and reduce your overall Azure spending. For more information, see [Advisor Cost recommendations](advisor-reference-cost-recommendations.md).
+* **Operational Excellence**: To help you achieve process and workflow efficiency, resource manageability and deployment best practices. For more information, see [Advisor Operational Excellence recommendations](advisor-reference-operational-excellence-recommendations.md).
 
 You can apply filters to display recommendations for specific subscriptions and resource types.
 
@@ -65,8 +65,8 @@ To learn more about Advisor recommendations, see:
 
 * [Get started with Advisor](advisor-get-started.md)
 * [Advisor score](azure-advisor-score.md)
-* [Advisor Reliability recommendations](advisor-high-availability-recommendations.md)
+* [Advisor Reliability recommendations](advisor-reference-reliability-recommendations.md)
 * [Advisor Security recommendations](advisor-security-recommendations.md)
-* [Advisor Performance recommendations](advisor-performance-recommendations.md)
-* [Advisor Cost recommendations](advisor-cost-recommendations.md)
-* [Advisor operational excellence recommendations](advisor-operational-excellence-recommendations.md)
+* [Advisor Performance recommendations](advisor-reference-performance-recommendations.md)
+* [Advisor Cost recommendations](advisor-reference-cost-recommendations.md)
+* [Advisor Operational Excellence recommendations](advisor-reference-operational-excellence-recommendations.md)

articles/ai-services/.openpublishing.redirection.ai-services.json

@@ -2,7 +2,12 @@
 	"redirections": [
         {
             "source_path_from_root": "/articles/ai-services/computer-vision/how-to/install-sdk.md",
-            "redirect_url": "/articles/ai-services/computer-vision/sdk/install-sdk",
+            "redirect_url": "/azure/ai-services/computer-vision/sdk/install-sdk",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/ai-services/document-intelligence/concept-document-intelligence-studio.md",
+            "redirect_url": "/azure/ai-services/document-intelligence/studio-overview",
             "redirect_document_id": false
         }
     ]

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

@@ -8,7 +8,7 @@ ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 04/16/2023
+ms.date: 07/09/2024
 ms.author: lajanuar
 ---
 
@@ -21,13 +21,11 @@ ms.author: lajanuar
 > * **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 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.
+Custom template models generate an estimated accuracy score when trained. Documents analyzed with a custom model produce a confidence score for extracted fields. A confidence score indicates probability by measuring the degree of statistical certainty that the extracted result is detected correctly. The estimated accuracy is calculated by running a few different combinations of the training data to predict the labeled values. 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
 
-The output of a `build` (v3.0) or `train` (v2.1) custom model operation includes the estimated accuracy score. This score represents the model's ability to accurately predict the labeled value on a visually similar document.
-The accuracy value range is a percentage between 0% (low) and 100% (high). The estimated accuracy is calculated by running a few different combinations of the training data to predict the labeled values.
+The output of a `build` (v3.0) or `train` (v2.1) custom model operation includes the estimated accuracy score. This score represents the model's ability to accurately predict the labeled value on a visually similar document. Accuracy is measured within a percentage value range from 0% (low) to 100% (high). It's best to target a score of 80% or higher. For more sensitive cases, like financial or medical records, we recommend a score of close to 100%. You can also require human review. 
 
 **Document Intelligence Studio** </br>
 **Trained custom model (invoice)**
@@ -50,6 +48,20 @@ Field confidence indicates an estimated probability between 0 and 1 that the pre
 
 :::image type="content" source="media/accuracy-confidence/confidence-scores.png" alt-text="confidence scores from Document Intelligence Studio":::
 
+## Improve confidence scores
+
+After an analysis operation, review the JSON output. Examine the `confidence` values for each key/value result under the `pageResults` node. You should also look at the confidence score in the `readResults` node, which corresponds to the text-read operation. The confidence of the read results doesn't affect the confidence of the key/value extraction results, so you should check both. Here are some tips:
+
+* If the confidence score for the `readResults` object is low, improve the quality of your input documents.
+
+* If the confidence score for the `pageResults` object is low, ensure that the documents you're analyzing are of the same type.
+
+* Consider incorporating human review into your workflows.
+
+* Use forms that have different values in each field.
+
+* For custom models, use a larger set of training documents. Tagging more documents teaches your model to recognize fields with greater accuracy.
+
 ## 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.

articles/ai-services/document-intelligence/concept-composed-models.md

@@ -8,7 +8,7 @@ ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 05/23/2024
+ms.date: 07/09/2024
 ms.author: lajanuar
 ---
 
@@ -55,6 +55,16 @@ With the introduction of [**custom classification models**](./concept-custom-cla
 > [!NOTE]
 > With the addition of **_custom neural model_** , there are a few limits to the compatibility of models that can be composed together.
 
+* With the model compose operation, you can assign up to 200 models to a single model ID. If the number of models that I want to compose exceeds the upper limit of a composed model, you can use one of these alternatives:
+
+  * Classify the documents before calling the custom model. You can use the [Read model](concept-read.md) and build a classification based on the extracted text from the documents and certain phrases by using sources like code, regular expressions, or search.
+
+  * If you want to extract the same fields from various structured, semi-structured, and unstructured documents, consider using the deep-learning [custom neural model](concept-custom-neural.md). Learn more about the [differences between the custom template model and the custom neural model](concept-custom.md#compare-model-features).
+
+* Analyzing a document by using composed models is identical to analyzing a document by using a single model. The `Analyze Document` result returns a `docType` property that indicates which of the component models you selected for analyzing the document. There's no change in pricing for analyzing a document by using an individual custom model or a composed custom model.
+
+* Model Compose is currently available only for custom models trained with labels.
+
 ### Composed model compatibility
 
 |Custom model type|Models trained with v2.1 and v2.0 | Custom template models v3.0 |Custom neural models 3.0|Custom Neural models v3.1|

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

@@ -6,7 +6,7 @@ author: vkurpad
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 02/29/2024
+ms.date: 07/09/2024
 ms.author: lajanuar
 ms.custom:
   - references_regions
@@ -49,9 +49,9 @@ Custom classification models are deep-learning-model types that combine layout a
 
 Custom classification models can analyze a single- or multi-file documents to identify if any of the trained document types are contained within an input file. Here are the currently supported scenarios:
 
-* A single file containing one document. For instance, a loan application form.
+* A single file containing one document type, such as a loan application form.
 
-* A single file containing multiple documents. For instance, a loan application package containing a loan application form, payslip, and bank statement.
+* A single file containing multiple document types. For instance, a loan application package that contains a loan application form, payslip, and bank statement.
 
 * A single file containing multiple instances of the same document. For instance, a collection of scanned invoices.
 
@@ -98,7 +98,7 @@ Classification models can now be trained on documents of different languages. Se
 
 Supported file formats:
 
-|Model | PDF |Image:<br>jpeg/jpg, png, bmp, tiff, heif| Microsoft Office:<br> Word (docx), Excel (xlxs), PowerPoint (pptx)|
+|Model | PDF |Image:<br>`jpeg/jpg`, `png`, `bmp`, `tiff`, `heif`| Microsoft Office:<br> Word (docx), Excel (xlxs), PowerPoint (pptx)|
 |--------|:----:|:-----:|:---------------:|
 |Read            | ✔    | ✔    | ✔  |
 |Layout          | ✔  | ✔ | ✔ (2024-02-29-preview, 2023-10-31-preview, and later)  |
@@ -130,7 +130,7 @@ Supported file formats:
 When you have more than one document in a file, the classifier can identify the different document types contained within the input file. The classifier response contains the page ranges for each of the identified document types contained within a file. This response can include multiple instances of the same document type.
 
 ::: moniker range=">=doc-intel-4.0.0"
-The analyze operation now includes a `splitMode` property that gives you granular control over the splitting behavior.
+The `analyze` operation now includes a `splitMode` property that gives you granular control over the splitting behavior.
 
 * To treat the entire input file as a single document for classification set the splitMode to `none`. When you do so, the service returns just one class for the entire input file.
 * To classify each page of the input file, set the splitMode to `perPage`. The service attempts to classify each page as an individual document.