Merge pull request #143 from MicrosoftDocs/main

9/5/2024 AM Publish

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

@@ -394,6 +394,11 @@
       "source_path_from_root": "/articles/ai-services/speech-service/how-to-configure-rhel-centos-7.md",
       "redirect_url": "/azure/ai-services/speech-service/quickstarts/setup-platform",
       "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",
+      "redirect_document_id": false
     }
   ]
 }

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

@@ -50,8 +50,6 @@
                 maintainContext: true
     - name: Multivariate Anomaly Detection
       items:
-        - name: REST API with Postman
-          href: how-to/postman.md
         - name: Create an Anomaly Detector Resource
           href: how-to/create-resource.md
         - name: Prepare and upload your data

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

@@ -1,5 +1,5 @@
 ---
-title: "Custom categories in Azure AI Content Safety"
+title: "Custom categories in Azure AI Content Safety (preview)"
 titleSuffix: Azure AI services
 description: Learn about custom content categories and the different ways you can use Azure AI Content Safety to handle them on your platform.
 #services: cognitive-services
@@ -12,7 +12,7 @@ ms.date: 07/05/2024
 ms.author: pafarley
 ---
 
-# Custom categories 
+# Custom categories (preview)
 
 Azure AI Content Safety lets you create and manage your own content moderation categories for enhanced moderation and filtering that matches your specific policies or use cases.
 

articles/ai-services/content-safety/how-to/custom-categories-rapid.md

@@ -1,5 +1,5 @@
 ---
-title: "Use the custom categories (rapid) API"
+title: "Use the custom categories (rapid) API (preview)"
 titleSuffix: Azure AI services
 description: Learn how to use the custom categories (rapid) API to mitigate harmful content incidents quickly.
 #services: cognitive-services
@@ -13,7 +13,7 @@ ms.author: pafarley
 ---
 
 
-# Use the custom categories (rapid) API
+# Use the custom categories (rapid) API (preview)
 
 The custom categories (rapid) API lets you quickly respond to emerging harmful content incidents. You can define an incident with a few examples in a specific topic, and the service will start detecting similar content.
 

articles/ai-services/content-safety/how-to/custom-categories.md

@@ -1,5 +1,5 @@
 ---
-title: "Use the custom category API"
+title: "Use the custom category API (preview)"
 titleSuffix: Azure AI services
 description: Learn how to use the custom category API to create your own harmful content categories and train the Content Safety model for your use case.
 #services: cognitive-services
@@ -12,7 +12,7 @@ ms.date: 04/11/2024
 ms.author: pafarley
 ---
 
-# Use the custom categories (standard) API
+# Use the custom categories (standard) API (preview)
 
 
 The custom categories (standard) API lets you create your own content categories for your use case and train Azure AI Content Safety to detect them in new content.

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

@@ -44,7 +44,7 @@ landingContent:
         links:
           - text: Harm categories
             url: concepts/harm-categories.md
-          - text: Custom categories
+          - text: Custom categories (preview)
             url: concepts/custom-categories.md
       - linkListType: quickstart
         links:
@@ -54,7 +54,7 @@ landingContent:
             url: quickstart-image.md?pivots=programming-language-rest
       - linkListType: how-to-guide
         links:
-          - text: Use custom categories
+          - text: Use custom categories (preview)
             url: how-to/custom-categories.md
 
 
@@ -64,7 +64,7 @@ landingContent:
         links:
           - text: Harm categories
             url: concepts/harm-categories.md
-          - text: Custom categories
+          - text: Custom categories (preview)
             url: concepts/custom-categories.md
           - text: Groundedness detection
             url: concepts/groundedness.md
@@ -78,7 +78,7 @@ landingContent:
             url: quickstart-groundedness.md
       - linkListType: how-to-guide
         links:
-          - text: Use custom categories
+          - text: Use custom categories (preview)
             url: how-to/custom-categories.md
           - text: Use a blocklist
             url: how-to/use-blocklist.md

articles/ai-services/content-safety/overview.md

@@ -123,7 +123,7 @@ See the following list for the input requirements for each feature.
 - **Protected material detection API**: 
   - Default maximum length: 1K characters.
   - Default minimum length: 110 characters (for scanning LLM completions, not user prompts).
-- **Custom categories (standard) API**:
+- **Custom categories (standard) API (preview)**:
   - Maximum inference input length: 1K characters.
 
 

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

@@ -1,5 +1,5 @@
 ---
-title: "Quickstart: Custom categories"
+title: "Quickstart: Custom categories (preview)"
 titleSuffix: Azure AI services
 description: Use the custom category API to create your own harmful content categories and train the Content Safety model for your use case.
 #services: cognitive-services
@@ -11,7 +11,7 @@ ms.date: 07/03/2024
 ms.author: pafarley
 ---
 
-# Quickstart: Custom categories (standard mode)
+# Quickstart: Custom categories (standard mode) (preview)
 
 Follow this guide to use Azure AI Content Safety Custom category REST API to create your own content categories for your use case and train Azure AI Content Safety to detect them in new text content.
 

articles/ai-services/content-safety/quickstart-jailbreak.md

@@ -11,7 +11,7 @@ ms.date: 03/15/2024
 ms.author: pafarley
 ---
 
-# Quickstart: Prompt Shields (preview)
+# Quickstart: Prompt Shields
 
 "Prompt Shields" in Azure AI Content Safety are specifically designed to safeguard generative AI systems from generating harmful or inappropriate content. These shields detect and mitigate risks associated with both User Prompt Attacks (malicious or harmful user-generated inputs) and Document Attacks (inputs containing harmful content embedded within documents). The use of "Prompt Shields" is crucial in environments where GenAI is employed, ensuring that AI outputs remain safe, compliant, and trustworthy.
 

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

@@ -23,7 +23,7 @@ items:
     href: concepts/groundedness.md
   - name: Protected material detection
     href: concepts/protected-material.md
-  - name: Custom categories
+  - name: Custom categories (preview)
     href: concepts/custom-categories.md
   - name: Harm categories
     href: concepts/harm-categories.md
@@ -35,7 +35,7 @@ items:
     href: quickstart-groundedness.md
   - name: Protected material detection
     href: quickstart-protected-material.md
-  - name: Custom categories
+  - name: Custom categories (preview)
     href: quickstart-custom-categories.md
   - name: Content Safety Studio
     href: studio-quickstart.md
@@ -57,7 +57,7 @@ items:
     href: /legal/cognitive-services/content-safety/data-privacy?context=/azure/ai-services/content-safety/context/context
 - name: How-to guides
   items:
-  - name: Use custom categories
+  - name: Use custom categories (preview)
     href: how-to/custom-categories.md
   - name: Use a blocklist
     href: how-to/use-blocklist.md

articles/ai-services/content-safety/whats-new.md

@@ -18,13 +18,13 @@ Learn what's new in the service. These items might be release notes, videos, blo
 
 ## July 2024
 
-### Custom categories (standard) API
+### Custom categories (standard) API public preview
 
 The custom categories (standard) API lets you create and train your own custom content categories and scan text for matches. See [Custom categories](./concepts/custom-categories.md) to learn more.
 
 ## May 2024
 
-### Custom categories (rapid) API
+### Custom categories (rapid) API public preview
 
 The custom categories (rapid) API lets you quickly define emerging harmful content patterns and scan text and images for matches. See [Custom categories](./concepts/custom-categories.md) to learn more.
 

articles/ai-services/openai/concepts/understand-embeddings.md

@@ -6,7 +6,7 @@ description: Learn more about how the Azure OpenAI embeddings API uses cosine si
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: tutorial
-ms.date: 09/12/2023
+ms.date: 09/05/2024
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -19,17 +19,11 @@ An embedding is a special format of data representation that machine learning mo
 
 ## Embedding models
 
-Different Azure OpenAI embedding models are created to be good at a particular task:
-
-- **Similarity embeddings** are good at capturing semantic similarity between two or more pieces of text.
-- **Text search embeddings** help measure whether long documents are relevant to a short query. 
-- **Code search embeddings** are useful for embedding code snippets and embedding natural language search queries.
-
 Embeddings make it easier to do machine learning on large inputs representing words by capturing the semantic similarities in a vector space. Therefore, you can use embeddings to determine if two text chunks are semantically related or similar, and provide a score to assess similarity.
 
 ## Cosine similarity
 
-Azure OpenAI embeddings rely on cosine similarity to compute similarity between documents and a query.
+Azure OpenAI embeddings often rely on cosine similarity to compute similarity between documents and a query.
 
 From a mathematic perspective, cosine similarity measures the cosine of the angle between two vectors projected in a multidimensional space. This measurement is beneficial, because if two documents are far apart by Euclidean distance because of size, they could still have a smaller angle between them and therefore higher cosine similarity. For more information about cosine similarity equations, see [Cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity).
 

articles/ai-services/openai/how-to/business-continuity-disaster-recovery.md

@@ -6,7 +6,7 @@ description: Considerations for implementing Business Continuity and Disaster Re
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 8/17/2023
+ms.date: 9/05/2024
 author: mrbullwinkle    
 ms.author: mbullwin
 recommendations: false

articles/ai-services/openai/how-to/chatgpt.md

@@ -7,7 +7,7 @@ ms.author: mbullwin #delegenz
 ms.service: azure-ai-openai
 ms.custom: build-2023, build-2023-dataai, devx-track-python
 ms.topic: how-to
-ms.date: 04/05/2024
+ms.date: 09/05/2024
 manager: nitinme
 keywords: ChatGPT
 ---

articles/ai-services/openai/how-to/integrate-synapseml.md

@@ -7,9 +7,9 @@ manager: nitinme
 ms.service: azure-ai-openai
 ms.custom: build-2023, build-2023-dataai
 ms.topic: how-to
-ms.date: 09/01/2023
-author: ChrisHMSFT
-ms.author: chrhoder
+ms.date: 09/01/2024
+author: mrbullwinkle
+ms.author: mbullwin
 recommendations: false
 ---
 

articles/ai-services/openai/quickstart.md

@@ -9,7 +9,7 @@ ms.custom: devx-track-dotnet, devx-track-python, devx-track-extended-java, devx-
 ms.topic: quickstart
 author: mrbullwinkle
 ms.author: mbullwin
-ms.date: 08/23/2023
+ms.date: 09/05/2024
 zone_pivot_groups: openai-quickstart-new
 recommendations: false
 ---

articles/machine-learning/how-to-access-azureml-behind-firewall.md

@@ -212,7 +212,7 @@ The hosts in this section are used to install Visual Studio Code packages to est
 | `code.visualstudio.com` | Required to download and install VS Code desktop. This host isn't required for VS Code Web. |
 | `update.code.visualstudio.com`<br>`*.vo.msecnd.net` | Used to retrieve VS Code server bits that are installed on the compute instance through a setup script. |
 | `marketplace.visualstudio.com`<br>`vscode.blob.core.windows.net`<br>`*.gallerycdn.vsassets.io` | Required to download and install VS Code extensions. These hosts enable the remote connection to compute instances using the Azure Machine Learning extension for VS Code. For more information, see [Connect to an Azure Machine Learning compute instance in Visual Studio Code](./how-to-set-up-vs-code-remote.md) |
-| `raw.githubusercontent.com/microsoft/vscode-tools-for-ai/master/azureml_remote_websocket_server/*` | Used to retrieve websocket server bits that are installed on the compute instance. The websocket server is used to transmit requests from Visual Studio Code client (desktop application) to Visual Studio Code server running on the compute instance. |
+| `https://github.com/microsoft/vscode-tools-for-ai/tree/master/azureml_remote_websocket_server/*` | Used to retrieve websocket server bits that are installed on the compute instance. The websocket server is used to transmit requests from Visual Studio Code client (desktop application) to Visual Studio Code server running on the compute instance. |
 | `vscode.download.prss.microsoft.com` | Used for Visual Studio Code download CDN |
 
 ### Ports