Merge branch 'main' into release-preview-new-mistral-models

Author: v-alje

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

@@ -155,7 +155,7 @@ To use the Content Safety APIs, you must create your Azure AI Content Safety res
 |--------------------|--------------------|--------------------|-------|-----------------|-------------------|---------------|---------------------------|---------------------------|------|-------------|
 | Australia East                     | ✅                  |                    | ✅    |                 | ✅                 | ✅             | ✅                         | ✅                         | ✅    | ✅           |
 | Canada East                          |                    |                    | ✅    |                 | ✅                 | ✅             | ✅                         | ✅                         | ✅    | ✅           |
-| Central US                             |                    |                    | ✅    |                 | ✅                 | ✅             | ✅                         | ✅                         | ✅    | ✅           |
+| Central US                             |                    |                    | ✅    |                 | ✅                 |             | ✅                         | ✅                         | ✅    | ✅           |
 | East US                            | ✅                 |                  | ✅    | ✅              | ✅                 | ✅             | ✅                         | ✅                         | ✅    | ✅           |
 | East US 2                           |                    | ✅                 | ✅    |                 | ✅                 | ✅             | ✅                         | ✅                         | ✅    | ✅           |
 | France Central                        |                    | ✅                 | ✅    |                 | ✅                 | ✅             | ✅                         | ✅                         | ✅    | ✅           |

articles/ai-services/language-service/personally-identifiable-information/includes/identification-entities.md

@@ -1922,13 +1922,13 @@ The following entities are grouped and listed by country/region:
     :::column-end:::
     :::column span="2":::
 
-        To get this entity category, add `UKNationalInsuranceNumber` to the `piiCategories` parameter. `UKNationalInsuranceNumber` will be returned in the API response if detected.
+        To get this entity category, add `UKElectoralRollNumber` to the `piiCategories` parameter. `UKElectoralRollNumber` will be returned in the API response if detected.
       
         Also returned with `domain=phi`.
     :::column-end:::
     :::column span="":::
 
-      `en`
+      `en`, `es`, `fr`, `de`, `it`, `pt-pt`, `pt-br`, `zh`, `ja`, `ko`, `nl`, `sv`, `tr`, `hi`, `da`, `nl`, `no`, `ro`, `ar`, `bg`, `hr`, `ms`, `ru`, `sl`, `cs`, `et`, `fi`, `he`, `hu`, `lv`, `sk`, `th`, `uk`
       
    :::column-end:::
 :::row-end:::

articles/ai-studio/how-to/develop/ai-template-get-started.md

@@ -35,8 +35,8 @@ Start with our sample applications! Choose the right template for your needs, th
 | [Process Automation: speech to text and summarization with Azure AI Foundry](https://github.com/Azure-Samples/summarization-openai-python-prompflow) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [Azure AI speech to text service](../../../ai-services/speech-service/index-speech-to-text.yml), Bicep  | An app for workers to report issues via text or speech, translating audio to text, summarizing it, and specify the relevant department. | 
 | [Multi-Modal Creative Writing copilot with Dalle](https://github.com/Azure-Samples/agent-openai-python-prompty) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure AI Search](/azure/search/search-what-is-azure-search), [Azure OpenAI Service](../../../ai-services/openai/overview.md), Bicep | demonstrates how to create and work with AI agents. The app takes a topic and instruction input and then calls a research agent, writer agent, and editor agent. |  
 | [Assistant API Analytics Copilot with Python and Azure AI Foundry](https://github.com/Azure-Samples/assistant-data-openai-python-promptflow) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) |  [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure AI Search](/azure/search/search-what-is-azure-search), [Azure OpenAI Service](../../../ai-services/openai/overview.md), Bicep| A data analytics chatbot based on the Assistants API. The chatbot can answer questions in natural language, and interpret them as queries on an example sales dataset. |
-| [Function Calling with Prompty, LangChain, and Pinecone](https://github.com/Azure-Samples/agent-openai-python-prompty-langchain-pinecone) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [LangChain](https://python.langchain.com/v0.1/docs/get_started/introduction), [Pinecone](https://www.pinecone.io/), Bicep  | Utilize the new Prompty tool, LangChain, and Pinecone to build a large language model (LLM) search agent. This agent with Retrieval-Augmented Generation (RAG) technology is capable of answering user questions based on the provided data by integrating real-time information retrieval with generative responses. | 
-| [Function Calling with Prompty, LangChain, and Elastic Search](https://github.com/Azure-Samples/agent-python-openai-prompty-langchain) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [Elastic Search](https://www.elastic.co/elasticsearch), [LangChain](https://python.langchain.com/v0.1/docs/get_started/introduction) , Bicep  | Utilize the new Prompty tool, LangChain, and Elasticsearch to build a large language model (LLM) search agent. This agent with Retrieval-Augmented Generation (RAG) technology is capable of answering user questions based on the provided data by integrating real-time information retrieval with generative responses |
+| Function Calling with Prompty, LangChain, and Pinecone | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [LangChain](https://python.langchain.com/v0.1/docs/get_started/introduction), [Pinecone](https://www.pinecone.io/), Bicep  | Utilize the new Prompty tool, LangChain, and Pinecone to build a large language model (LLM) search agent. This agent with Retrieval-Augmented Generation (RAG) technology is capable of answering user questions based on the provided data by integrating real-time information retrieval with generative responses. | 
+| Function Calling with Prompty, LangChain, and Elastic Search | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [Elastic Search](https://www.elastic.co/elasticsearch), [LangChain](https://python.langchain.com/v0.1/docs/get_started/introduction) , Bicep  | Utilize the new Prompty tool, LangChain, and Elasticsearch to build a large language model (LLM) search agent. This agent with Retrieval-Augmented Generation (RAG) technology is capable of answering user questions based on the provided data by integrating real-time information retrieval with generative responses |
 
 ### [C#](#tab/csharp)
 

articles/ai-studio/how-to/develop/sdk-overview.md

@@ -428,7 +428,7 @@ if application_insights_connection_string:
 
 ::: zone pivot="programming-language-csharp"
 
-Tracing is not yet integrated into the projects package. For instructions on how to instrument and log traces from the Azure AI Inferencing package, see [azure-sdk-for-dotnet](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/ai/Azure.AI.Inference/samples/Sample8_ChatCompletionsWithOpenTelemetry.md.).
+Tracing is not yet integrated into the projects package. For instructions on how to instrument and log traces from the Azure AI Inferencing package, see [azure-sdk-for-dotnet](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/ai/Azure.AI.Inference/samples/Sample8_ChatCompletionsWithOpenTelemetry.md).
 
 ::: zone-end
 

articles/ai-studio/includes/ai-services/code-create-chat-client.md

@@ -55,7 +55,7 @@ const client = new ModelClient(
 );
 ```
 
-Explore our [samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples) and read the [API reference documentation](https://aka.ms/AAp1kxa) to get yourself started.
+Explore our [samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples) and read the [API reference documentation](/azure/ai-services/openai/gpt-v-quickstart) to get yourself started.
 
 # [C#](#tab/csharp)
 

articles/ai-studio/toc.yml

@@ -93,8 +93,6 @@ items:
       href: how-to/concept-data-privacy.md
     - name: Model lifecycle and retirement
       href: concepts/model-lifecycle-retirement.md
-    - name: Model benchmarks
-      href: how-to/model-benchmarks.md
     - name: Model benchmarking
       items:
       - name: Model benchmarks

articles/ai-studio/what-is-ai-studio.md

@@ -70,7 +70,7 @@ For more information, see [Management center overview](./concepts/management-cen
 
 Azure AI Foundry is monetized through individual products customer access and consume in the platform, including API and models, complete AI toolchain, and responsible AI and enterprise grade production at scale products. Each product has its own billing model and price. 
 
-The platform is free to use and explore. Pricing occurs at deployment level. For more information abut AI Foundry pricing, see [AI Foundry pricing](https://aka.ms/Azure-AI-Foundry-New-Pricing-Page).
+The platform is free to use and explore. Pricing occurs at deployment level. 
 
 Using AI Foundry also incurs cost associated with the underlying services. To learn more, read [Plan and manage costs for Azure AI services](./how-to/costs-plan-manage.md).
 

articles/machine-learning/how-to-train-distributed-gpu.md

@@ -115,7 +115,7 @@ TF_CONFIG='{
 
 ### TensorFlow example
 
-* For the full notebook to run the TensorFlow example, see [azureml-examples: Train a basic neural network with distributed MPI on the MNIST dataset using Tensorflow with Horovod](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/single-step/tensorflow/mnist-distributed-horovod/tensorflow-mnist-distributed-horovod.ipynb).
+* For the full notebook to run the TensorFlow example, see [azureml-examples: Train a basic neural network with distributed MPI on the MNIST dataset using Tensorflow with Horovod](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/single-step/tensorflow/mnist-distributed/tensorflow-mnist-distributed.ipynb).
 
 ## Accelerating distributed GPU training with InfiniBand
 

articles/machine-learning/v1/how-to-monitor-datasets.md

@@ -252,7 +252,7 @@ monitor = monitor.enable_schedule()
 ```
 
 > [!TIP]
-> For a full example of setting up a `timeseries` dataset and data drift detector, see our [example notebook](https://aka.ms/datadrift-notebook).
+> For a full example of setting up a `timeseries` dataset and data drift detector, see our [example notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb).
 
 
 # [Studio](#tab/azure-studio)
@@ -573,6 +573,6 @@ Limitations and known issues for data drift monitors:
 
 ## Next steps
 
-* Head to the [Azure Machine Learning studio](https://ml.azure.com) or the [Python notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb) to set up a dataset monitor.
+* Head to the [Azure Machine Learning studio](https://ml.azure.com) or the [Python notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb) to set up a dataset monitor.
 * See how to set up data drift on [models deployed to Azure Kubernetes Service](how-to-enable-data-collection.md).
 * Set up dataset drift monitors with [Azure Event Grid](../how-to-use-event-grid.md).

articles/machine-learning/v1/how-to-train-tensorflow.md

@@ -35,9 +35,9 @@ Run this code on either of these environments:
 - Your own Jupyter Notebook server
     - [Install the Azure Machine Learning SDK](/python/api/overview/azure/ml/install) (>= 1.15.0).
     - [Create a workspace configuration file](how-to-configure-environment.md).
-    - [Download the sample script files](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow) `tf_mnist.py` and `utils.py`
+    - [Download the sample script files](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow) `tf_mnist.py` and `utils.py`
 
-    You can also find a completed [Jupyter Notebook version](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) of this guide on the GitHub samples page. The notebook includes expanded sections covering intelligent hyperparameter tuning, model deployment, and notebook widgets.
+    You can also find a completed [Jupyter Notebook version](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb) of this guide on the GitHub samples page. The notebook includes expanded sections covering intelligent hyperparameter tuning, model deployment, and notebook widgets.
 
 [!INCLUDE [gpu quota](../includes/machine-learning-gpu-quota-prereq.md)]