Merge branch 'main' into release-document-intelligence-restructure

Author: v-alje

articles/ai-services/language-service/concepts/model-lifecycle.md

@@ -43,11 +43,11 @@ Use the table below to find which model versions are supported by each feature:
 | Entity Linking                                      | `latest*`                                      |                                             |
 | Named Entity Recognition (NER)                      | `latest*`                                      | `2023-04-15-preview**`                      |
 | Personally Identifiable Information (PII) detection | `latest*`                                      | `2023-04-15-preview**`                      | 
-| PII detection for conversations (Preview)           | `latest*`                                      | `2023-04-15-preview**`                      |
+| PII detection for conversations                     | `latest*`                                      | `2023-04-15-preview**`                      |
 | Question answering                                  | `latest*`                                      |                                             |
 | Text Analytics for health                           | `latest*`                                      | `2022-08-15-preview`, `2023-01-01-preview**`|
 | Key phrase extraction                               | `latest*`                                      |                                             | 
-| Summarization                              |  `latest*`                                      |                       |
+| Summarization                                       |  `latest*`                                     |                                             |
 
 
 \* Latest Generally Available (GA) model version

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: 09/05/2024
+ms.date: 10/6/2024
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -15,7 +15,7 @@ ms.custom:
 
 # Understand embeddings in Azure OpenAI Service
 
-An embedding is a special format of data representation that machine learning models and algorithms can easily use. The embedding is an information dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating-point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar. Embeddings power vector similarity search in Azure Databases such as [Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/vector-search) ,  [Azure SQL Database](/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql&preserve-view=true#vector-search) or [Azure Database for PostgreSQL - Flexible Server](/azure/postgresql/flexible-server/how-to-use-pgvector).
+An embedding is a special format of data representation that machine learning models and algorithms can easily use. The embedding is an information dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating-point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar. Embeddings power vector similarity search in retrieval systems such as [Azure AI Search](/azure/search) (recommended) and in Azure databases such as [Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/vector-search) ,  [Azure SQL Database](/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql&preserve-view=true#vector-search), and [Azure Database for PostgreSQL - Flexible Server](/azure/postgresql/flexible-server/how-to-use-pgvector).
 
 ## Embedding models
 

articles/machine-learning/concept-automl-forecasting-sweeping.md

@@ -66,7 +66,7 @@ AutoML follows the usual cross-validation procedure, training a separate model o
 
 Cross-validation for forecasting jobs is configured by setting the number of cross-validation folds, and optionally, the number of time periods between two consecutive cross-validation folds. For more information and an example of configuring cross-validation for forecasting, see [Custom cross-validation settings](how-to-auto-train-forecast.md#custom-cross-validation-settings).
 
-You can also bring your own validation data. For more information, see [Configure training, validation, cross-validation, and test data in AutoML (SDK v1)](./v1/how-to-configure-cross-validation-data-splits.md#provide-validation-data).
+You can also bring your own validation data. For more information, see [Configure training, validation, cross-validation, and test data in AutoML (SDK v1)](./v1/how-to-configure-cross-validation-data-splits.md#provide-validation-dataset).
 
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