Merge branch 'MicrosoftDocs:main' into patch-1

Author: longb1

articles/ai-services/document-intelligence/overview.md

@@ -487,6 +487,7 @@ You can use Document Intelligence to automate document processing in application
 > [!div class="nextstepaction"]
 > [Return to custom model types](#custom-models)
 
+::: moniker range=">=doc-intel-3.1.0"
 #### Custom classification model
 
 :::image type="content" source="media/overview/custom-classifier-labeling.png" alt-text="{alt-text}":::

articles/ai-services/openai/how-to/function-calling.md

@@ -51,6 +51,7 @@ To force the model to call a specific function set the `tool_choice` parameter w
 > The default behavior (`tool_choice: "auto"`) is for the model to decide on its own whether to call a function and if so which function to call.
 
 ```python
+import os
 from openai import AzureOpenAI
 import json
 
@@ -60,8 +61,6 @@ client = AzureOpenAI(
   api_version="2023-12-01-preview"
 )
 
-from openai import OpenAI
-import json
 
 # Example function hard coded to return the same weather
 # In production, this could be your backend API or an external API

articles/ai-services/speech-service/personal-voice-how-to-use.md

@@ -29,7 +29,7 @@ Here's example SSML in a request for text to speech with the voice name and the
 
 ```xml
 <speak version='1.0' xmlns='http://www.w3.org/2001/10/synthesis' xmlns:mstts='http://www.w3.org/2001/mstts' xml:lang='en-US'>
-    <voice xml:lang='en-US' xml:gender='Male' name='PhoenixV2Neural'> 
+    <voice name='PhoenixV2Neural'> 
     <mstts:ttsembedding speakerProfileId='your speaker profile ID here'> 
     I'm happy to hear that you find me amazing and that I have made your trip planning easier and more fun. 我很高兴听到你觉得我很了不起，我让你的旅行计划更轻松、更有趣。Je suis heureux d'apprendre que vous me trouvez incroyable et que j'ai rendu la planification de votre voyage plus facile et plus amusante.  
     </mstts:ttsembedding> 

articles/ai-studio/concepts/ai-resources.md

@@ -8,7 +8,7 @@ ms.custom:
   - ignite-2023
 ms.topic: conceptual
 ms.date: 12/14/2023
-ms.reviewer: eur
+ms.reviewer: Blackmist
 ms.author: eur
 author: eric-urban
 ---

articles/ai-studio/concepts/deployments-overview.md

@@ -2,15 +2,15 @@
 title: Deploy models, flows, and web apps with Azure AI Studio
 titleSuffix: Azure AI Studio
 description: Learn about deploying models, flows, and web apps with Azure AI Studio.
-manager: nitinme
+manager: scottpolly
 ms.service: azure-ai-studio
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
 ms.date: 12/7/2023
-ms.reviewer: eur
-ms.author: eur
-author: eric-urban
+ms.reviewer: fasantia
+ms.author: mopeakande
+author: msakande
 ---
 
 # Overview: Deploy models, flows, and web apps with Azure AI Studio
@@ -76,7 +76,7 @@ The model or flow that you deploy can be used in a web application hosted in Azu
 
 ## Planning AI safety for a deployed model
 
-For Azure OpenAI models such as GPT-4, Azure AI Studio provides AI safety filter during the deployment to ensure responsible use of AI. AI content safety filter allows moderation of harmful and sensitive contents to promote the safety of AI-enhanced applications. In addition to AI safety filter, Azure AI Studio offers model monitoring for deployed models. Model monitoring for LLMs uses the latest GPT language models to monitor and alert when the outputs of the model perform poorly against the set thresholds of generation safety and quality. For example, you can configure a monitor to evaluate how well the model’s generated answers align with information from the input source ("groundedness") and closely match to a ground truth sentence or document ("similarity"). 
+For Azure OpenAI models such as GPT-4, Azure AI Studio provides AI safety filter during the deployment to ensure responsible use of AI. AI content safety filter allows moderation of harmful and sensitive contents to promote the safety of AI-enhanced applications. In addition to AI safety filter, Azure AI Studio offers model monitoring for deployed models. Model monitoring for LLMs uses the latest GPT language models to monitor and alert when the outputs of the model perform poorly against the set thresholds of generation safety and quality. For example, you can configure a monitor to evaluate how well the model's generated answers align with information from the input source ("groundedness") and closely match to a ground truth sentence or document ("similarity"). 
 
 ## Optimizing the performance of a deployed model
 

articles/ai-studio/how-to/connections-add.md

@@ -8,7 +8,7 @@ ms.custom:
   - ignite-2023
 ms.topic: how-to
 ms.date: 11/15/2023
-ms.reviewer: eur
+ms.reviewer: Blackmist
 ms.author: eur
 author: eric-urban
 ---

articles/ai-studio/how-to/deploy-models-llama.md

@@ -2,13 +2,13 @@
 title: How to deploy Llama 2 family of large language models with Azure AI Studio
 titleSuffix: Azure AI Studio
 description: Learn how to deploy Llama 2 family of large language models with Azure AI Studio.
-manager: nitinme
+manager: scottpolly
 ms.service: azure-ai-studio
 ms.topic: how-to
 ms.date: 12/11/2023
-ms.reviewer: eur
-ms.author: eur
-author: eric-urban
+ms.reviewer: fasantia
+ms.author: mopeakande
+author: msakande
 ms.custom: [references_regions]
 ---
 
@@ -440,7 +440,7 @@ Each time a project subscribes to a given offer from the Azure Marketplace, a ne
 
 :::image type="content" source="../media/cost-management/marketplace/costs-model-as-service-cost-details.png" alt-text="A screenshot showing different resources corresponding to different model offers and their associated meters."  lightbox="../media/cost-management/marketplace/costs-model-as-service-cost-details.png":::
 
-Quota is managed per deployment. Each deployment has a rate limit of 20,000 tokens per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits don’t suffice your scenarios.
+Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits don't suffice your scenarios.
 
 ### Considerations for Llama 2 models deployed as real-time endpoints
 

articles/ai-studio/how-to/deploy-models-open.md

@@ -2,13 +2,13 @@
 title: How to deploy open models with Azure AI Studio
 titleSuffix: Azure AI Studio
 description: Learn how to deploy open models with Azure AI Studio.
-manager: nitinme
+manager: scottpolly
 ms.service: azure-ai-studio
 ms.topic: how-to
 ms.date: 12/11/2023
-ms.reviewer: eur
-ms.author: eur
-author: eric-urban
+ms.reviewer: fasantia
+ms.author: mopeakande
+author: msakande
 ---
 
 # How to deploy large language models with Azure AI Studio 

articles/ai-studio/how-to/deploy-models-openai.md

@@ -2,15 +2,15 @@
 title: How to deploy Azure OpenAI models with Azure AI Studio
 titleSuffix: Azure AI Studio
 description: Learn how to deploy Azure OpenAI models with Azure AI Studio.
-manager: nitinme
+manager: scottpolly
 ms.service: azure-ai-studio
 ms.custom:
   - ignite-2023
 ms.topic: how-to
 ms.date: 12/11/2023
-ms.reviewer: eur
-ms.author: eur
-author: eric-urban
+ms.reviewer: fasantia
+ms.author: mopeakande
+author: msakande
 ---
 
 # How to deploy Azure OpenAI models with Azure AI Studio

articles/ai-studio/how-to/index-add.md

@@ -8,7 +8,7 @@ ms.custom:
   - ignite-2023
 ms.topic: how-to
 ms.date: 11/15/2023
-ms.reviewer: eur
+ms.reviewer: Blackmist
 ms.author: eur
 author: eric-urban
 ---