Merge pull request #279348 from HeidiSteen/heidist-june24

[azure search] AML skill preview status

Author: prmerger-automator[bot]

articles/search/cognitive-search-aml-skill.md

@@ -10,19 +10,23 @@ ms.custom:
   - ignite-2023
   - build-2024
 ms.topic: reference
-ms.date: 05/28/2024
+ms.date: 06/26/2024
 ---
 
 # AML skill in an Azure AI Search enrichment pipeline
 
-> [!IMPORTANT] 
-> This skill is in public preview under [supplemental terms of use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [preview REST API](/rest/api/searchservice/index-preview) supports this skill.
+> [!IMPORTANT]
+> This skill is in public preview under [supplemental terms of use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). Preview REST APIs support this skill.
 
 The **AML** skill allows you to extend AI enrichment with a custom [Azure Machine Learning (AML)](../machine-learning/overview-what-is-azure-machine-learning.md) model. Once an AML model is [trained and deployed](../machine-learning/concept-azure-machine-learning-architecture.md#workspace), an **AML** skill integrates it into AI enrichment.
 
 Like other built-in skills, an **AML** skill has inputs and outputs. The inputs are sent to your deployed AML online endpoint as a JSON object, which outputs a JSON payload as a response along with a success status code. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed. The response is expected to have the outputs specified by your **AML** skill. Any other response is considered an error and no enrichments are performed.
 
-If you're using the [Azure AI Studio model catalog vectorizer (preview)](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) for integrated vectorization at query time, you should also use the **AML** skill for integrated vectorization during indexing. See [How to implement integrated vectorization using models from Azure AI Studio](vector-search-integrated-vectorization-ai-studio.md) for instructions. This scenario is supported through the 2024-05-01-preview REST API and the Azure portal.
+The **AML** skill is a preview feature, but depending on the endpoint, you can call it in a skillset that targets a stable API version. For example, a skillset that's created using 2023-11-01 stable API can include an **AML** skill even though it's a preview feature.
+
+Starting in 2024-05-01-preview REST API and in the Azure portal (which also targets the 2024-05-01-preview), Azure AI Search introduced the [Azure AI Studio model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) for query time connections to the model catalog in Azure AI Studio. If you want to use that vectorizer for queries, the **AML** skill is the *indexing counterpart* for generating embeddings using a model in the Azure AI Studio model catalog. 
+
+During indexing, the **AML** skill can connect to the model catalog to generate vectors for the index. At query time, queries can use a vectorizer to connect to the same model to vectorize text strings for a vector query. In this workflow, the **AML** skill and the model catalog vectorizer should be used together so that you're using the same embedding model for both indexing and queries. See [How to implement integrated vectorization using models from Azure AI Studio](vector-search-integrated-vectorization-ai-studio.md) for details on this workflow.
 
 > [!NOTE]
 > The indexer will retry twice for certain standard HTTP status codes returned from the AML online endpoint. These HTTP status codes are:

articles/search/search-api-preview.md

@@ -28,13 +28,13 @@ Preview features are removed from this list if they're retired or transition to
 |  [**Scalar quantization**](vector-search-how-to-configure-compression-storage.md#option-1-configure-scalar-quantization) | Index | Compress vector index size in memory and on disk using built-in scalar quantization. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) to add a `compressions` section to a vector profile. |
 |  [**Narrow data types**](vector-search-how-to-configure-compression-storage.md#option-2-assign-narrow-data-types-to-vector-fields) | Index | Assign a smaller data type on vector fields, assuming incoming data is of that data type. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) to specify a vector field definition. [Binary vector support](vector-search-how-to-index-binary-data.md) is added in 2024-05-01-preview.|
 |  [**stored property**](vector-search-how-to-configure-compression-storage.md#option-3-set-the-stored-property-to-remove-retrievable-storage) | Index | Boolean that reduces storage of vector indexes by *not* storing retrievable vectors. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) to set `stored` on a vector field. |
-|  [**Vectorizers**](vector-search-integrated-vectorization.md) | Queries | Text-to-vector conversion during query execution. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) to define a `vectorizer`. [Search POST (preview)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true) for `vectorQueries`. |
+|  [**Vectorizers**](vector-search-integrated-vectorization.md) | Queries | Text-to-vector conversion during query execution. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) to define a `vectorizer`. [Search POST (preview)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true) for `vectorQueries`. Vectorizers should be paired with an equivalent skill that supports integrated vectorization during indexing. Skills used for embeddings during indexing include AzureOpenAIEmbedding, Azure AI Vision multimodal, AML for models in the Azure AI Studio model catalog. There are vectorizers that correspond to each one of these embedding skills. Always use the same embedding model for both queries and indexing. |
 |  [**Integrated vectorization**](vector-search-integrated-vectorization.md) | Index, skillset | Skills-driven data chunking and embedding during indexing. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) for AzureOpenAIEmbedding skill and the data chunking properties of the Text Split skill. |
 | [**Import and vectorize data**](search-get-started-portal-import-vectors.md) | Azure portal | A wizard that creates a full indexing pipeline that includes data chunking and vectorization. The wizard creates all of the objects and configuration settings. | Available on all search services, in all regions. |
 | [**AzureOpenAIEmbedding skill**](cognitive-search-skill-azure-openai-embedding.md) | Applied AI (skills) | A new skill type that calls Azure OpenAI embedding model to generate embeddings during queries and indexing. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). Also available in the portal through the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md). |
 | [**Azure AI Vision multimodal embedding skill**](cognitive-search-skill-vision-vectorize.md) | Applied AI (skills) | A new skill type that calls Azure AI Vision multimodal API to generate embeddings for text or images during indexing. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
-| [**Text Split skill**](cognitive-search-skill-textsplit.md) | Applied AI (skills) | Text Split has two new chunking-related properties in preview: `maximumPagesToTake`, `pageOverlapLength`. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). Also available in the portal through the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md). |
-| [**Azure Machine Learning (AML) skill**](cognitive-search-aml-skill.md) | Applied AI (skills) | A new skill type to integrate an inferencing endpoint from Azure Machine Learning. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). Starting in the 2024-05-01-preview, you can use this skill to connect to a model in the Azure AI Studio model catalog. It's also available in the portal, in skillset design, assuming Azure AI Search and Azure Machine Learning services are deployed in the same subscription. |
+| [**Text Split skill**](cognitive-search-skill-textsplit.md) | Applied AI (skills) | Text Split has two new chunking-related properties in preview: `maximumPagesToTake`, `pageOverlapLength`. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) adds support for the preview properties. These properties are also used in the portal through the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md). |
+| [**Azure Machine Learning (AML) skill**](cognitive-search-aml-skill.md) | Applied AI (skills) | AML skill integrates an inferencing endpoint from Azure Machine Learning. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). In previous preview APIs, it supports connections to deployed custom models in an AML workspace. Starting in the 2024-05-01-preview, you can use this skill in workflows that connect to embedding models in the Azure AI Studio model catalog. It's also available in the portal, in skillset design, assuming Azure AI Search and Azure Machine Learning services are deployed in the same subscription. |
 | [**Incremental enrichment**](cognitive-search-incremental-indexing-conceptual.md) | Applied AI (skills) | Adds caching to an enrichment pipeline, allowing you to reuse existing output if a targeted modification, such as an update to a skillset or another object, doesn't change the content. Caching applies only to enriched documents produced by a skillset.| [Create or Update Indexer (preview)](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
 | [**Index projections**](index-projections-concept-intro.md) | Applied AI (skills) | A component of a skillset definition that defines the shape of a secondary index, supporting a one-to-many index pattern, where content from an enrichment pipeline can target multiple indexes.| [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). Also available in the portal through the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md). |
 |  [**OneLake files indexer**](search-how-to-index-onelake-files.md) | Indexer data source | New data source for extracting searchable data and metadata data from a [lakehouse](/fabric/onelake/create-lakehouse-onelake) on top of [OneLake](/fabric/onelake/onelake-overview) | [Create or Update Data Source (preview)](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |