Merge branch 'main' into patch-1

Author: jonburchel

articles/machine-learning/breadcrumb/toc.yml

@@ -1,3 +1,4 @@
+items:
 - name: Azure
   tocHref: /azure/
   topicHref: /azure/index
@@ -73,3 +74,19 @@
         tocHref: /azure/devops/pipelines/languages/
         topicHref: /azure/devops/pipelines/index
 
+- name: Azure
+  tocHref: /security/
+  topicHref: /azure/index
+  items:
+  - name: Machine Learning
+    tocHref: /security/benchmark/
+    topicHref: /azure/machine-learning/index
+    items:
+    - name: Service
+      tocHref: /security/benchmark/azure/baselines/
+      topicHref: /azure/machine-learning/index
+      items:
+      - name: Security benchmarks
+        tocHref: /security/benchmark/azure/
+        topicHref: /security/benchmark/azure/index
+ 

articles/machine-learning/toc.yml

@@ -1,10 +1,13 @@
+items:
 - name: Azure Machine Learning Documentation
   href: index.yml
 - name: Overview
   items:
     - name: What is Azure Machine Learning?
       displayName: AML, services, overview, introduction
       href: overview-what-is-azure-machine-learning.md
+    - name: "AI Studio or AML: Which should I choose?"
+      href: /ai/ai-studio-experiences-overview?toc=/azure/machine-learning/toc.json&bc=/azure/machine-learning/breadcrumb/toc.json
     - name: Azure Machine Learning CLI and Python SDK
       href: concept-v2.md
     # v1
@@ -1807,8 +1810,8 @@
       href: https://stackoverflow.com/questions/tagged/azure-machine-learning-service
     - name: Compare our ML products
       href: /azure/architecture/data-guide/technology-choices/data-science-and-machine-learning?toc=/azure/machine-learning/toc.json&bc=/azure/machine-learning/breadcrumb/toc.json
-      displayName: accessibility, designer, visual interface, screen reader, keyboard shortcuts
     - name: Designer accessibility features
+      displayName: accessibility, designer, visual interface, screen reader, keyboard shortcuts
       href: designer-accessibility.md
     # v1
     - name: Python SDK (v1) release notes

articles/machine-learning/tutorial-deploy-model.md

@@ -9,7 +9,7 @@ ms.topic: tutorial
 author: msakande
 ms.author: mopeakande
 ms.reviewer: sehan
-ms.date: 03/04/2024
+ms.date: 09/13/2024
 ms.custom: mlops, devx-track-python #add more custom tags
 #Customer intent: This tutorial is intended to show users what is needed for deployment and present a high-level overview of how Azure Machine Learning handles deployment. Deployment isn't typically done by a data scientist, so the tutorial won't use Azure CLI examples. We will link to existing articles that use Azure CLI as needed. The code in the tutorial will use SDK v2. The tutorial will continue where the "Create reusable pipelines" tutorial stops.
 ---
@@ -96,7 +96,7 @@ If you already completed the earlier training tutorial, [Train a model](tutorial
 
 If you didn't complete the training tutorial, you need to register the model. Registering your model before deployment is a recommended best practice.
 
-The following code specifies the `path` (where to upload files from) inline. If you [cloned the tutorials folder](quickstart-create-resources.md#learn-from-sample-notebooks), then run the following code as-is. Otherwise, [download the files and metadata for the model to deploy](https://azuremlexampledata.blob.core.windows.net/datasets/credit_defaults_model.zip) and unzip the files. Update the path to the location of the unzipped files on your local computer.
+The following code specifies the `path` (where to upload files from) inline. If you [cloned the tutorials folder](quickstart-create-resources.md#learn-from-sample-notebooks), then run the following code as-is. Otherwise, download the files and metadata for the model from the [credit_defaults_model folder](https://github.com/Azure/azureml-examples/tree/main/tutorials/get-started-notebooks/deploy/credit_defaults_model). Save the files you downloaded into a local version of the *credit_defaults_model* folder on your computer and update the path in the following code to the location of the downloaded files.
 
 The SDK automatically uploads the files and registers the model. 
 

articles/search/cognitive-search-common-errors-warnings.md

@@ -22,7 +22,7 @@ Indexing stops when the error count exceeds ['maxFailedItems'](cognitive-search-
 If you want indexers to ignore these errors (and skip over "failed documents"), consider updating the `maxFailedItems` and `maxFailedItemsPerBatch` as described [here](/rest/api/searchservice/indexers/create#indexingparameters).
 
 > [!NOTE]
-> Each failed document along with its document key (when available) will show up as an error in the indexer execution status. You can utilize the [index api](/rest/api/searchservice/addupdate-or-delete-documents) to manually upload the documents at a later point if you have set the indexer to tolerate failures.
+> Each failed document along with its document key (when available) will show up as an error in the indexer execution status. You can utilize the [index api](/rest/api/searchservice/documents) to manually upload the documents at a later point if you have set the indexer to tolerate failures.
 
 The error information in this article can help you resolve errors, allowing indexing to continue.
 
@@ -385,7 +385,7 @@ Output field mappings that reference non-existent/null data will produce warning
 <a name="the-data-change-detection-policy-is-configured-to-use-key-column-x"></a>
 
 ## `Warning: The data change detection policy is configured to use key column 'X'`
-[Data change detection policies](/rest/api/searchservice/create-data-source#data-change-detection-policies) have specific requirements for the columns they use to detect change. One of these requirements is that this column is updated every time the source item is changed. Another requirement is that the new value for this column is greater than the previous value. Key columns don't fulfill this requirement because they don't change on every update. To work around this issue, select a different column for the change detection policy.
+[Data change detection policies](/rest/api/searchservice/data-sources/create#request-body) have specific requirements for the columns they use to detect change. One of these requirements is that this column is updated every time the source item is changed. Another requirement is that the new value for this column is greater than the previous value. Key columns don't fulfill this requirement because they don't change on every update. To work around this issue, select a different column for the change detection policy.
 
 <a name="document-text-appears-to-be-utf-16-encoded-but-is-missing-a-byte-order-mark"></a>
 

articles/search/cognitive-search-concept-intro.md

@@ -116,7 +116,7 @@ An enrichment pipeline consists of [*indexers*](search-indexer-overview.md) that
 
 Start with a subset of data in a [supported data source](search-indexer-overview.md#supported-data-sources). Indexer and skillset design is an iterative process. The work goes faster with a small representative data set.
 
-1. Create a [data source](/rest/api/searchservice/create-data-source) that specifies a connection to your data.
+1. Create a [data source](/rest/api/searchservice/data-sources/create) that specifies a connection to your data.
 
 1. [Create a skillset](cognitive-search-defining-skillset.md). Unless your project is small, you should [attach an Azure AI multi-service resource](cognitive-search-attach-cognitive-services.md). If you're [creating a knowledge store](knowledge-store-create-rest.md), define it within the skillset.
 

articles/search/cognitive-search-tutorial-blob.md

@@ -97,7 +97,7 @@ AI enrichment is indexer-driven. This part of the walkthrough creates four objec
 
 ### Step 1: Create a data source
 
-Call [Create Data Source](/rest/api/searchservice/create-data-source) to set the connection string to the Blob container containing the sample data files.
+Call [Create Data Source](/rest/api/searchservice/data-sources/create) to set the connection string to the Blob container containing the sample data files.
 
 ```http
 ### Create a data source

articles/search/index-add-suggesters.md

@@ -152,7 +152,7 @@ private static void CreateIndex(string indexName, SearchIndexClient indexClient)
 A suggester is used in a query. After a suggester is created, call one of the following APIs for a search-as-you-type experience:
 
 + [Suggestions REST API](/rest/api/searchservice/suggestions)
-+ [Autocomplete REST API](/rest/api/searchservice/autocomplete)
++ [Autocomplete REST API](/rest/api/searchservice/documents/autocomplete-post)
 + [SuggestAsync method](/dotnet/api/azure.search.documents.searchclient.suggestasync)
 + [AutocompleteAsync method](/dotnet/api/azure.search.documents.searchclient.autocompleteasync)
 

articles/search/index-similarity-and-scoring.md

@@ -137,7 +137,9 @@ In Azure AI Search, you can configure BM25 algorithm parameters, and tune search
 
 ## featuresMode parameter (preview)
 
-[Search Documents](/rest/api/searchservice/preview-api/search-documents) requests have a new [featuresMode](/rest/api/searchservice/preview-api/search-documents#featuresmode) parameter that can provide more detail about relevance at the field level. Whereas the `@searchScore` is calculated for the document all-up (how relevant is this document in the context of this query), through featuresMode you can get information about individual fields, as expressed in a `@search.features` structure. The structure contains all fields used in the query (either specific fields through **searchFields** in a query, or all fields attributed as **searchable** in an index). For each field, you get the following values:
+[Search Documents](/rest/api/searchservice/documents/search-post) requests support a featuresMode parameter that provides more detail about a relevance score at the field level. Whereas the `@searchScore` is calculated for the document all-up (how relevant is this document in the context of this query), featuresMode reveals information about individual fields, as expressed in a `@search.features` structure. The structure contains all fields used in the query (either specific fields through **searchFields** in a query, or all fields attributed as **searchable** in an index). 
+
+For each field, `@search.features` give you the following values:
 
 + Number of unique tokens found in the field
 + Similarity score, or a measure of how similar the content of the field is, relative to the query term
@@ -167,6 +169,8 @@ For a query that targets the "description" and "title" fields, a response that i
 
 You can consume these data points in [custom scoring solutions](https://github.com/Azure-Samples/search-ranking-tutorial) or use the information to debug search relevance problems.
 
+The featuresMode parameter isn't documented in the REST APIs, but you can use it on a preview REST API call to Search Documents.
+
 ## Number of ranked results in a full text query response
 
 By default, if you aren't using pagination, the search engine returns the top 50 highest ranking matches for full text search. You can use the `top` parameter to return a smaller or larger number of items (up to 1000 in a single response). Full text search is subject to a maximum limit of 1,000 matches (see [API response limits](search-limits-quotas-capacity.md#api-response-limits)). Once 1,000 matches are found, the search engine no longer looks for more.

articles/search/knowledge-store-create-rest.md

@@ -113,7 +113,7 @@ A valid API key establishes trust, on a per request basis, between the applicati
 
 ## Create a data source
 
-[Create Data Source](/rest/api/searchservice/create-data-source) creates a data source connection on Azure AI Search.
+[Create Data Source](/rest/api/searchservice/data-sources/create) creates a data source connection on Azure AI Search.
 
 1. Paste in the following example to create the data source.
 

articles/search/monitor-azure-cognitive-search-data-reference.md

@@ -194,7 +194,7 @@ The following operations can appear in a resource log.
 | Indexers.* | Applies to an indexer. Can be Create, Delete, Get, List, and Status. |
 | Indexes.* | Applies to a search index. Can be Create, Delete, Get, List.  |
 | indexes.Prototype | This index is created by the Import Data wizard. |
-| Indexing.Index  | This operation is a call to [Add, Update or Delete Documents](/rest/api/searchservice/addupdate-or-delete-documents). |
+| Indexing.Index  | This operation is a call to [Index Documents](/rest/api/searchservice/documents). |
 | Metadata.GetMetadata | A request for search service system data.  |
 | Query.Autocomplete | An autocomplete query against an index. See [Query types and composition](search-query-overview.md). |
 | Query.Lookup |  A lookup query against an index. See [Query types and composition](search-query-overview.md). |