fixing merge conflict

Author: aahill

.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",
@@ -40,6 +45,11 @@
       "redirect_url": "/azure/ai-services/custom-vision-service/whats-new",
       "redirect_document_id": false
     },
+    {
+      "source_path_from_root": "/articles/ai-services/custom-vision-service/concepts/compare-alternatives.md",
+      "redirect_url": "/azure/ai-services/custom-vision-service/overview",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/ai-services/luis/luis-migration-authoring.md",
       "redirect_url": "/azure/ai-services/language-service/conversational-language-understanding/how-to/migrate-from-luis",
@@ -405,6 +415,21 @@
       "redirect_url": "/azure/ai-services/speech-service/release-notes",
       "redirect_document_id": false
     },
+    {
+      "source_path_from_root": "/articles/ai-services/speech-service/get-started-speaker-recognition.md",
+      "redirect_url": "/azure/ai-services/speech-service/speaker-recognition-overview",
+      "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",
@@ -414,6 +439,26 @@
       "source_path_from_root": "/articles/ai-services/anomaly-detector//tutorials/multivariate-anomaly-detection-synapse.md",
       "redirect_url": "/azure/ai-services/anomaly-detector/overview",
       "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/language-service/summarization/custom/how-to/data-formats.md",
+      "redirect_url": "/azure/ai-services//language-service/summarization/overview",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/language-service/summarization/custom/how-to/deploy-model.md",
+      "redirect_url": "/azure/ai-services//language-service/summarization/overview",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/language-service/summarization/custom/how-to/test-evaluate.md",
+      "redirect_url": "/azure/ai-services//language-service/summarization/overview",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/language-service/summarization/custom/quickstart.md",
+      "redirect_url": "/azure/ai-services//language-service/summarization/overview",
+      "redirect_document_id": false
     }
   ]
 }

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/cognitive-services-container-support.md

@@ -7,7 +7,7 @@ author: aahill
 manager: nitinme
 ms.service: azure-ai-services
 ms.topic: overview
-ms.date: 09/17/2024
+ms.date: 09/25/2024
 ms.author: aahi
 keywords: on-premises, Docker, container, Kubernetes
 #Customer intent: As a potential customer, I want to know more about how Azure AI services provides and supports Docker containers for each service.

articles/ai-services/computer-vision/concept-describe-images-40.md

@@ -8,35 +8,37 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 09/25/2024
 ms.author: pafarley
 ---
 
 # Image captions (version 4.0)
-Image captions in Image Analysis 4.0 are available through the **Caption** and **Dense Captions** features. 
 
-Caption generates a one-sentence description for all image contents. Dense Captions provides more detail by generating one-sentence descriptions of up to 10 regions of the image in addition to describing the whole image. Dense Captions also returns bounding box coordinates of the described image regions. Both these features use the latest groundbreaking Florence-based AI models. 
+Image captions in Image Analysis 4.0 are available through the **Caption** and **Dense Captions** features.
 
-At this time, image captioning is available in English only.
+The Caption feature generates a one-sentence description of all the image contents. Dense Captions provides more detail by generating one-sentence descriptions of up to 10 different regions of the image in addition to describing the whole image. Dense Captions also returns bounding box coordinates of the described image regions. Both of these features use the latest Florence-based AI models.
+
+Image captioning is available in English only.
 
 > [!IMPORTANT]
-> Image captioning in Image Analysis 4.0 is only available in certain Azure data center regions: see [Region availability](./overview-image-analysis.md#region-availability). You must use a Vision resource located in one of these regions to get results from Caption and Dense Captions features.
+> Image captioning in Image Analysis 4.0 is only available in certain Azure data center regions: see [Region availability](./overview-image-analysis.md#region-availability). You must use an Azure AI Vision resource located in one of these regions to get results from Caption and Dense Captions features.
 >
-> If you have to use a Vision resource outside these regions to generate image captions, please use [Image Analysis 3.2](concept-describing-images.md) which is available in all Azure AI Vision regions.  
+> If you need to use a Vision resource outside these regions to generate image captions, please use [Image Analysis 3.2](concept-describing-images.md) which is available in all Azure AI Vision regions.
 
 Try out the image captioning features quickly and easily in your browser using Vision Studio.
 
 > [!div class="nextstepaction"]
 > [Try Vision Studio](https://portal.vision.cognitive.azure.com/)
 
-### Gender-neutral captions
-Captions contain gender terms ("man", "woman", "boy" and "girl") by default. You have the option to replace these terms with "person" in your results and receive gender-neutral captions. You can do so by setting the optional API request parameter, **gender-neutral-caption** to `true` in the request URL.
+## Gender-neutral captions
+
+By default, captions contain gender terms ("man", "woman", "boy" and "girl"). You have the option to replace these terms with "person" in your results and receive gender-neutral captions. You can do so by setting the optional API request parameter `gender-neutral-caption` to `true` in the request URL.
 
 ## Caption and Dense Captions examples
 
 #### [Caption](#tab/image)
 
-The following JSON response illustrates what the Analysis 4.0 API returns when describing the example image based on its visual features.
+The following JSON response illustrates what the Image Analysis 4.0 API returns when describing the example image based on its visual features.
 
 ![Photo of a man pointing at a screen](./Media/quickstarts/presentation.png)
 
@@ -51,7 +53,7 @@ The following JSON response illustrates what the Analysis 4.0 API returns when d
 
 #### [Dense Captions](#tab/dense)
 
-The following JSON response illustrates what the Analysis 4.0 API returns when generating dense captions for the example image.
+The following JSON response illustrates what the Image Analysis 4.0 API returns when generating dense captions for the example image.
 
 ![Photo of a tractor on a farm](./Images/farm.png)
 

articles/ai-services/computer-vision/concept-describing-images.md

@@ -8,15 +8,15 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 04/30/2024
+ms.date: 09/25/2024
 ms.author: pafarley
 ---
 
 # Image descriptions
 
-Azure AI Vision can analyze an image and generate a human-readable phrase that describes its contents. The algorithm returns several descriptions based on different visual features, and each description is given a confidence score. The final output is a list of descriptions ordered from highest to lowest confidence.
+Azure AI Vision can analyze an image and generate a human-readable phrase that describes its contents. The service returns several descriptions based on different visual features, and each description is given a confidence score. The final output is a list of descriptions ordered from highest to lowest confidence.
 
-At this time, English is the only supported language for image description.
+English is the only supported language for image descriptions.
 
 Try out the image captioning features quickly and easily in your browser using Vision Studio.
 
@@ -25,7 +25,7 @@ Try out the image captioning features quickly and easily in your browser using V
 
 ## Image description example
 
-The following JSON response illustrates what the Analyze API returns when describing the example image based on its visual features.
+The following JSON response illustrates what the Analyze Image API returns when describing the example image based on its visual features.
 
 ![A black and white picture of buildings in Manhattan](./Images/bw_buildings.png)
 

articles/ai-services/computer-vision/concept-image-retrieval.md

@@ -1,24 +1,24 @@
 ---
 title: Multimodal embeddings concepts - Image Analysis 4.0
 titleSuffix: Azure AI services
-description: Concepts related to image vectorization using the Image Analysis 4.0 API.
+description: Learn about concepts related to image vectorization and search/retrieval using the Image Analysis 4.0 API.
 #services: cognitive-services
 author: PatrickFarley
 manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 02/20/2024
+ms.date: 09/25/2024
 ms.author: pafarley
 ---
 
 # Multimodal embeddings (version 4.0)
 
-Multimodal embedding is the process of generating a numerical representation of an image that captures its features and characteristics in a vector format. These vectors encode the content and context of an image in a way that is compatible with text search over the same vector space.
+Multimodal embedding is the process of generating a vector representation of an image that captures its features and characteristics. These vectors encode the content and context of an image in a way that is compatible with text search over the same vector space.
 
-Image retrieval systems have traditionally used features extracted from the images, such as content labels, tags, and image descriptors, to compare images and rank them by similarity. However, vector similarity search is gaining more popularity due to a number of benefits over traditional keyword-based search and is becoming a vital component in popular content search services.
+Image retrieval systems have traditionally used features extracted from the images, such as content labels, tags, and image descriptors, to compare images and rank them by similarity. However, vector similarity search offers a number of benefits over traditional keyword-based search and is becoming a vital component in popular content search services.
 
-## What's the difference between vector search and keyword-based search? 
+## Differences between vector search and keyword search
 
 Keyword search is the most basic and traditional method of information retrieval. In that approach, the search engine looks for the exact match of the keywords or phrases entered by the user in the search query and compares it with the labels and tags provided for the images. The search engine then returns images that contain those exact keywords as content tags and image labels. Keyword search relies heavily on the user's ability to use relevant and specific search terms.
 
@@ -50,18 +50,17 @@ Each dimension of the vector corresponds to a different feature or attribute of
 
 The following are the main steps of the image retrieval process using Multimodal embeddings.
 
-:::image type="content" source="media/image-retrieval.png" alt-text="Diagram of image retrieval process.":::
+:::image type="content" source="media/image-retrieval.png" alt-text="Diagram of the multimodal embedding / image retrieval process.":::
 
 1. Vectorize Images and Text: the Multimodal embeddings APIs, **VectorizeImage** and **VectorizeText**, can be used to extract feature vectors out of an image or text respectively. The APIs return a single feature vector representing the entire input.
    > [!NOTE]
    > Multimodal embedding does not do any biometric processing of human faces. For face detection and identification, see the [Azure AI Face service](./overview-identity.md).
-
 1. Measure similarity: Vector search systems typically use distance metrics, such as cosine distance or Euclidean distance, to compare vectors and rank them by similarity. The [Vision studio](https://portal.vision.cognitive.azure.com/) demo uses [cosine distance](./how-to/image-retrieval.md#calculate-vector-similarity) to measure similarity.  
 1. Retrieve Images: Use the top _N_ vectors similar to the search query and retrieve images corresponding to those vectors from your photo library to  provide as the final result.
 
 ### Relevance score 
 
-The image and video retrieval services return a field called "relevance." The term "relevance" denotes a measure of similarity score between a query and image or video frame embeddings. The relevance score is composed of two parts:
+The image and video retrieval services return a field called "relevance." The term "relevance" denotes a measure of similarity between a query and image or video frame embeddings. The relevance score is composed of two parts:
 1. The cosine similarity (that falls in the range of [0,1]) between the query and image or video frame embeddings.
 1. A metadata score, which reflects the similarity between the query and the metadata associated with the image or video frame.
 

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