Merge branch 'MicrosoftDocs:main' into heidist-scoring

Author: HeidiSteen

.openpublishing.publish.config.json

@@ -3,13 +3,14 @@
     {
       "docset_name": "azure-ai",
       "build_source_folder": ".",
+      "build_output_subfolder": "azure-ai",
+      "locale": "en-us",
+      "monikers": [],
+      "moniker_ranges": [],
       "xref_query_tags": [
         "/dotnet",
         "/python"
       ],
-      "build_output_subfolder": "azure-ai",
-      "locale": "en-us",
-      "monikers": [],
       "open_to_public_contributors": true,
       "type_mapping": {
         "Conceptual": "Content",
@@ -172,4 +173,4 @@
   ],
   "branch_target_mapping": {},
   "targets": {}
-}
+}

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

@@ -30,6 +30,11 @@
       "redirect_url": "/azure/ai-services/language-service/conversational-language-understanding/how-to/migrate-from-luis",
       "redirect_document_id": false
     },
+    {
+      "source_path_from_root": "/articles/ai-services/luis/luis-concept-data-conversion.md",
+      "redirect_url": "/azure/ai-services/language-service/conversational-language-understanding/how-to/migrate-from-luis",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/ai-services/custom-vision-service/update-application-to-3.0-sdk.md",
       "redirect_url": "/azure/ai-services/custom-vision-service/overview",
@@ -405,6 +410,16 @@
       "redirect_url": "/azure/ai-services/speech-service/release-notes",
       "redirect_document_id": false
     },
+    {
+      "source_path_from_root": "/articles/ai-services/speech-service/how-to-recognize-intents-from-speech-csharp.md",
+      "redirect_url": "/azure/ai-services/speech-service/intent-recognition",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/speech-service/how-to-custom-speech-continuous-integration-continuous-deployment.md",
+      "redirect_url": "/azure/ai-services/speech-service/how-to-custom-speech-model-and-endpoint-lifecycle",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/ai-services/anomaly-detector/how-to/postman.md",
       "redirect_url": "/azure/ai-services/anomaly-detector/overview",

articles/ai-services/anomaly-detector/index.yml

@@ -12,7 +12,7 @@ metadata:
   manager: nitinme
   ms.service: azure-ai-anomaly-detector
   ms.topic: landing-page
-  ms.date: 01/18/2024
+  ms.date: 09/20/2024
   ms.author: mbullwin
 
 

articles/ai-services/anomaly-detector/overview.md

@@ -7,7 +7,7 @@ author: mrbullwinkle
 manager: nitinme
 ms.service: azure-ai-anomaly-detector
 ms.topic: overview
-ms.date: 01/18/2024
+ms.date: 09/20/2024
 ms.author: mbullwin
 keywords: anomaly detection, machine learning, algorithms
 ---

articles/ai-services/computer-vision/concept-object-detection-40.md

@@ -23,8 +23,6 @@ Try out the capabilities of object detection quickly and easily in your browser
 > [!div class="nextstepaction"]
 > [Try Vision Studio](https://portal.vision.cognitive.azure.com/)
 
-> [!TIP]
-> You can use the Object detection feature through the [Azure OpenAI](/azure/ai-services/openai/overview) service. The **GPT-4 Turbo with Vision** model lets you chat with an AI assistant that can analyze the images you share, and the Vision Enhancement option uses Image Analysis to provide the AI assistance with more details (readable text and object locations) about the image. For more information, see the [GPT-4 Turbo with Vision quickstart](/azure/ai-services/openai/gpt-v-quickstart).
 
 ## Object detection example
 

articles/ai-services/computer-vision/concept-ocr.md

@@ -23,8 +23,6 @@ OCR is a machine-learning-based technique for extracting text from in-the-wild a
 
 The new Azure AI Vision Image Analysis 4.0 REST API offers the ability to extract printed or handwritten text from images in a unified performance-enhanced synchronous API that makes it easy to get all image insights including OCR results in a single API operation. The Read OCR engine is built on top of multiple deep learning models supported by universal script-based models for [global language support](./language-support.md).
 
-> [!TIP]
-> You can also use the OCR feature in conjunction with the [Azure OpenAI](/azure/ai-services/openai/overview) service. The **GPT-4 Turbo with Vision** model lets you chat with an AI assistant that can analyze the images you share, and the Vision Enhancement option uses Image Analysis to give the AI assistant more details (readable text and object locations) about the image. For more information, see the [GPT-4 Turbo with Vision quickstart](/azure/ai-services/openai/gpt-v-quickstart).
 
 ## Text extraction example
 

articles/ai-services/computer-vision/includes/image-analysis-curl-quickstart-40.md

@@ -53,7 +53,7 @@ A successful response is returned in JSON, similar to the following example:
 
 ```json
 {
-    "modelVersion": "2024-02-01",
+    "modelVersion": "2023-10-01",
     "captionResult":
     {
         "text": "a man pointing at a screen",

articles/ai-services/computer-vision/overview-image-analysis.md

@@ -61,8 +61,6 @@ You can analyze images to provide insights about their visual features and chara
 |**Detect the color scheme** (v3.2 only) |Analyze color usage within an image. Azure AI Vision can determine whether an image is black & white or color and, for color images, identify the dominant and accent colors.| [Detect the color scheme](concept-detecting-color-schemes.md)|
 |**Moderate content in images** (v3.2 only) |You can use Azure AI Vision to detect adult content in an image and return confidence scores for different classifications. The threshold for flagging content can be set on a sliding scale to accommodate your preferences.|[Detect adult content](concept-detecting-adult-content.md)|
 
-> [!TIP]
-> You can leverage the Read text and Object detection features of Image Analysis through the [Azure OpenAI](/azure/ai-services/openai/overview) service. The **GPT-4 Turbo with Vision** model lets you chat with an AI assistant that can analyze the images you share, and the Vision Enhancement option uses Image Analysis to give the AI assistant more details about the image (readable text and object locations). For more information, see the [GPT-4 Turbo with Vision quickstart](/azure/ai-services/openai/gpt-v-quickstart).
 
 ## Product Recognition (v4.0 preview only) (deprecated)
 

articles/ai-services/content-safety/concepts/custom-categories.md

@@ -51,7 +51,7 @@ This implementation works on text content and image content.
 
 #### [Custom categories (standard) API](#tab/standard)
 
-The Azure AI Content Safety custom category feature uses a multi-step process for creating, training, and using custom content classification models. Here's a look at the workflow:
+The Azure AI Content Safety custom categories feature uses a multi-step process for creating, training, and using custom content classification models. Here's a look at the workflow:
 
 ### Step 1: Definition and setup
 
@@ -73,7 +73,7 @@ You use the **analyzeCustomCategory** API to analyze text content and determine
 
 #### [Custom categories (rapid) API](#tab/rapid)
 
-To use the custom category (rapid) API, you first create an **incident** object with a text description. Then, you upload any number of image or text samples to the incident. The LLM on the backend will then use these to evaluate future input content. No training step is needed.
+To use the custom categories (rapid) API, you first create an **incident** object with a text description. Then, you upload any number of image or text samples to the incident. The LLM on the backend will then use these to evaluate future input content. No training step is needed.
 
 You can include your defined incident in a regular text analysis or image analysis request. The service will indicate whether the submitted content is an instance of your incident. The service can still do other content moderation tasks in the same API call.
 

articles/ai-services/content-safety/faq.yml

@@ -40,7 +40,7 @@ sections:
       - question: |
           Can I detect harmful content in custom categories that I define myself?
         answer: |
-          Yes, you can use the Azure AI Content Safety custom categories API to create categories, upload examples, and train the service to detect similar content. Custom categories are available for both text and image moderation.
+          Yes, you can use the Azure AI Content Safety custom categories (standard) API to create categories, upload examples, and train the service to detect similar content. Custom categories are available for both text and image moderation.
       - question: |
           How does billing work for the Azure AI Content Safety service?
         answer: |