diff --git a/articles/ai-services/.openpublishing.redirection.ai-services.json b/articles/ai-services/.openpublishing.redirection.ai-services.json index 3a1fde5f15f..85b27a6dbc2 100644 --- a/articles/ai-services/.openpublishing.redirection.ai-services.json +++ b/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 } ] } \ No newline at end of file diff --git a/articles/ai-services/anomaly-detector/how-to/postman.md b/articles/ai-services/anomaly-detector/how-to/postman.md deleted file mode 100644 index bc45390fbc7..00000000000 --- a/articles/ai-services/anomaly-detector/how-to/postman.md +++ /dev/null @@ -1,61 +0,0 @@ ---- -title: How to run Multivariate Anomaly Detector API (GA version) in Postman? -titleSuffix: Azure AI services -description: Learn how to detect anomalies in your data either as a batch, or on streaming data with Postman. -#services: cognitive-services -author: mrbullwinkle -manager: nitinme -ms.service: azure-ai-anomaly-detector -ms.topic: how-to -ms.date: 01/18/2024 -ms.author: mbullwin ---- - -# How to run Multivariate Anomaly Detector API in Postman? - -[!INCLUDE [Deprecation announcement](../includes/deprecation.md)] - -This article will walk you through the process of using Postman to access the Multivariate Anomaly Detection REST API. - -## Getting started - -Select this button to fork the API collection in Postman and follow the steps in this article to test. - -[![Run in Postman](../media/postman/button.svg)](https://app.getpostman.com/run-collection/18763802-b90da6d8-0f98-4200-976f-546342abcade?action=collection%2Ffork&collection-url=entityId%3D18763802-b90da6d8-0f98-4200-976f-546342abcade%26entityType%3Dcollection%26workspaceId%3De1370b45-5076-4885-884f-e9a97136ddbc#?env%5BMVAD%5D=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) - -## Multivariate Anomaly Detector API - -1. Select environment as **MVAD**. - - :::image type="content" source="../media/postman/postman-initial.png" alt-text="Screenshot of Postman UI with MVAD selected." lightbox="../media/postman/postman-initial.png"::: - -2. Select **Environment**, paste your Anomaly Detector `endpoint`, `key` and dataSource `url` into the **CURRENT VALUE** column, select **Save** to let the variables take effect. - - :::image type="content" source="../media/postman/postman-key.png" alt-text="Screenshot of Postman UI with key, endpoint, and datasource filled in." lightbox="../media/postman/postman-key.png"::: - -3. Select **Collections**, and select the first API - **Create and train a model**, then select **Send**. - - > [!NOTE] - > If your data is one CSV file, please set the dataSchema as **OneTable**, if your data is multiple CSV files in a folder, please set the dataSchema as **MultiTable.** - - :::image type="content" source="../media/postman/create-and-train.png" alt-text="Screenshot of create and train POST request." lightbox="../media/postman/create-and-train.png"::: - -4. In the response of the first API, copy the modelId and paste it in the `modelId` in **Environments**, select **Save**. Then go to **Collections**, select **Get model status**, and select **Send**. - ![GIF of process of copying model identifier](../media/postman/model.gif) - -5. Select **Batch Detection**, and select **Send**. This API will trigger a synchronous inference task, and you should use the Get batch detection results API several times to get the status and the final results. - - :::image type="content" source="../media/postman/result.png" alt-text="Screenshot of batch detection POST request." lightbox="../media/postman/result.png"::: - -6. In the response, copy the `resultId` and paste it in the `resultId` in **Environments**, select **Save**. Then go to **Collections**, select **Get batch detection results**, and select **Send**. - - ![GIF of process of copying result identifier](../media/postman/result.gif) - -7. For the rest of the APIs calls, select each and then select Send to test out their request and response. - - :::image type="content" source="../media/postman/detection.png" alt-text="Screenshot of detect last POST result." lightbox="../media/postman/detection.png"::: - -## Next Steps - -* [Create an Anomaly Detector resource](create-resource.md) -* [Quickstart: Detect anomalies in your time series data using the Anomaly Detector](../quickstarts/client-libraries.md) diff --git a/articles/ai-services/anomaly-detector/media/postman/button.svg b/articles/ai-services/anomaly-detector/media/postman/button.svg deleted file mode 100644 index 231269a5433..00000000000 --- a/articles/ai-services/anomaly-detector/media/postman/button.svg +++ /dev/null @@ -1 +0,0 @@ - diff --git a/articles/ai-services/anomaly-detector/media/postman/create-and-train.png b/articles/ai-services/anomaly-detector/media/postman/create-and-train.png deleted file mode 100644 index df82711948c..00000000000 Binary files a/articles/ai-services/anomaly-detector/media/postman/create-and-train.png and /dev/null differ diff --git a/articles/ai-services/anomaly-detector/media/postman/detection.png b/articles/ai-services/anomaly-detector/media/postman/detection.png deleted file mode 100644 index cbfa02c5489..00000000000 Binary files a/articles/ai-services/anomaly-detector/media/postman/detection.png and /dev/null differ diff --git a/articles/ai-services/anomaly-detector/media/postman/model.gif b/articles/ai-services/anomaly-detector/media/postman/model.gif deleted file mode 100644 index 01cced20b68..00000000000 Binary files a/articles/ai-services/anomaly-detector/media/postman/model.gif and /dev/null differ diff --git a/articles/ai-services/anomaly-detector/media/postman/postman-initial.png b/articles/ai-services/anomaly-detector/media/postman/postman-initial.png deleted file mode 100644 index 13da82edd0e..00000000000 Binary files a/articles/ai-services/anomaly-detector/media/postman/postman-initial.png and /dev/null differ diff --git a/articles/ai-services/anomaly-detector/media/postman/postman-key.png b/articles/ai-services/anomaly-detector/media/postman/postman-key.png deleted file mode 100644 index 95cc3a698e4..00000000000 Binary files a/articles/ai-services/anomaly-detector/media/postman/postman-key.png and /dev/null differ diff --git a/articles/ai-services/anomaly-detector/media/postman/result.gif b/articles/ai-services/anomaly-detector/media/postman/result.gif deleted file mode 100644 index 63d83778cb2..00000000000 Binary files a/articles/ai-services/anomaly-detector/media/postman/result.gif and /dev/null differ diff --git a/articles/ai-services/anomaly-detector/media/postman/result.png b/articles/ai-services/anomaly-detector/media/postman/result.png deleted file mode 100644 index 16a511504ee..00000000000 Binary files a/articles/ai-services/anomaly-detector/media/postman/result.png and /dev/null differ diff --git a/articles/ai-services/anomaly-detector/toc.yml b/articles/ai-services/anomaly-detector/toc.yml index 45e87b1617c..56ab2f864cc 100644 --- a/articles/ai-services/anomaly-detector/toc.yml +++ b/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 diff --git a/articles/ai-services/content-safety/concepts/custom-categories.md b/articles/ai-services/content-safety/concepts/custom-categories.md index 6199fdb0ba8..d2cc1af2790 100644 --- a/articles/ai-services/content-safety/concepts/custom-categories.md +++ b/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. diff --git a/articles/ai-services/content-safety/how-to/custom-categories-rapid.md b/articles/ai-services/content-safety/how-to/custom-categories-rapid.md index dba2472bea6..5e3195b00e6 100644 --- a/articles/ai-services/content-safety/how-to/custom-categories-rapid.md +++ b/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. diff --git a/articles/ai-services/content-safety/how-to/custom-categories.md b/articles/ai-services/content-safety/how-to/custom-categories.md index 1ad41346504..7e26e9f2fb2 100644 --- a/articles/ai-services/content-safety/how-to/custom-categories.md +++ b/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. diff --git a/articles/ai-services/content-safety/index.yml b/articles/ai-services/content-safety/index.yml index 6a66c2f7125..361befc7495 100644 --- a/articles/ai-services/content-safety/index.yml +++ b/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 diff --git a/articles/ai-services/content-safety/overview.md b/articles/ai-services/content-safety/overview.md index 8c32a1a2230..261e9810ac4 100644 --- a/articles/ai-services/content-safety/overview.md +++ b/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. diff --git a/articles/ai-services/content-safety/quickstart-custom-categories.md b/articles/ai-services/content-safety/quickstart-custom-categories.md index a3d208ccb96..7374a9a8f31 100644 --- a/articles/ai-services/content-safety/quickstart-custom-categories.md +++ b/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. diff --git a/articles/ai-services/content-safety/quickstart-jailbreak.md b/articles/ai-services/content-safety/quickstart-jailbreak.md index c52ffe822be..f1f9b5f4483 100644 --- a/articles/ai-services/content-safety/quickstart-jailbreak.md +++ b/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. diff --git a/articles/ai-services/content-safety/toc.yml b/articles/ai-services/content-safety/toc.yml index eeda6217114..8957cecdd30 100644 --- a/articles/ai-services/content-safety/toc.yml +++ b/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 diff --git a/articles/ai-services/content-safety/whats-new.md b/articles/ai-services/content-safety/whats-new.md index 8a622a4deea..1a0b3a2a50b 100644 --- a/articles/ai-services/content-safety/whats-new.md +++ b/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. diff --git a/articles/ai-services/openai/concepts/understand-embeddings.md b/articles/ai-services/openai/concepts/understand-embeddings.md index 4e4740f3db9..f54250f1041 100644 --- a/articles/ai-services/openai/concepts/understand-embeddings.md +++ b/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). diff --git a/articles/ai-services/openai/how-to/business-continuity-disaster-recovery.md b/articles/ai-services/openai/how-to/business-continuity-disaster-recovery.md index 68e9c20b098..aaf9fde966e 100644 --- a/articles/ai-services/openai/how-to/business-continuity-disaster-recovery.md +++ b/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 diff --git a/articles/ai-services/openai/how-to/chatgpt.md b/articles/ai-services/openai/how-to/chatgpt.md index 8730f9a2e6c..b861624c03d 100644 --- a/articles/ai-services/openai/how-to/chatgpt.md +++ b/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 --- diff --git a/articles/ai-services/openai/how-to/integrate-synapseml.md b/articles/ai-services/openai/how-to/integrate-synapseml.md index 12a71a240b9..464a462e674 100644 --- a/articles/ai-services/openai/how-to/integrate-synapseml.md +++ b/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 --- diff --git a/articles/ai-services/openai/quickstart.md b/articles/ai-services/openai/quickstart.md index 9c647ff71ad..4d22415387b 100644 --- a/articles/ai-services/openai/quickstart.md +++ b/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 --- diff --git a/articles/machine-learning/how-to-access-azureml-behind-firewall.md b/articles/machine-learning/how-to-access-azureml-behind-firewall.md index d30277516f9..3702b274436 100644 --- a/articles/machine-learning/how-to-access-azureml-behind-firewall.md +++ b/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`
`*.vo.msecnd.net` | Used to retrieve VS Code server bits that are installed on the compute instance through a setup script. | | `marketplace.visualstudio.com`
`vscode.blob.core.windows.net`
`*.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 diff --git a/articles/search/search-limits-quotas-capacity.md b/articles/search/search-limits-quotas-capacity.md index ffcf85e7d08..82cc97b8dac 100644 --- a/articles/search/search-limits-quotas-capacity.md +++ b/articles/search/search-limits-quotas-capacity.md @@ -90,13 +90,15 @@ Vector limits vary by service creation date and tier. #### Storage quota (GB) -This table repeats [partition storage limits](#service-limits) for context. The table shows the progression of storage quota increases in GB over time. Vector quota is per partition, so the increase in vector quota is bound to the increase in per-partition storage for each tier. Higher capacity partitions were brought online starting in April 2024. +This table repeats [partition storage limits](#service-limits) for context. The table shows the progression of storage quota increases in GB over time. Vector quota is per partition, so the increase in vector quota is bound to the increase in per-partition storage for each tier. -| Service creation date |Basic | S1| S2 | S3 | L1 | L2 | +Higher capacity partitions were brought online starting in April 2024. Standard 3 (S3) and Standard 3 High Density (S3HD) have the same storage and partition limits. + +| Service creation date |Basic | S1| S2 | S3/HD | L1 | L2 | |-----------------------|------|---|----|----|----|----| -|**Before July 1, 2023** 1 | 2 | 25 | 100 | 200 | 1,000 | 2,000 | -| **July 1, 2023 through April 3, 2024** 2| 2 | 25 | 100 | 200 | 1,000 | 2,000 | -|**April 3, 2024 through May 17, 2024** 3 | 15 | 160 | 350 | 700 | 1,000 | 2,000 | +|**Before July 1, 2023** 1 | 2 | 25 | 100 | 200 | 1,024 | 2,048 | +|**July 1, 2023 through April 3, 2024** 2| 2 | 25 | 100 | 200 | 1,024 | 2,048 | +|**April 3, 2024 through May 17, 2024** 3 | 15 | 160 | 512 | 1,024 | 1,024 | 2,048 | |**After May 17, 2024** 4 | 15 | 160 | 512 | 1,024 | 2,048 | 4,096 | 1 Partition sizes during early preview. @@ -111,7 +113,7 @@ This table repeats [partition storage limits](#service-limits) for context. The This table shows the progression of vector quota increases in GB over time. The quota is per partition, so if you scale a new Standard (S1) service to 6 partitions, total vector quota is 35 multiplied by 6. -| Service creation date |Basic | S1| S2 | S3 | L1 | L2 | +| Service creation date |Basic | S1| S2 | S3/HD | L1 | L2 | |-----------------------|------|---|----|----|----|----| |**Before July 1, 2023** 1 | 0.5 | 1 | 6 | 12 | 12 | 36 | | **July 1, 2023 through April 3, 2024** 2| 1 | 3 | 12 | 36 | 12 | 36 |