Merge pull request #284968 from MicrosoftDocs/main

8/19/2024 AM Publish

Author: Taojunshen

.openpublishing.redirection.sentinel.json

@@ -1,5 +1,10 @@
 {
     "redirections": [
+        {
+            "source_path": "articles/sentinel/notebooks-troubleshoot.md",
+            "redirect_url": "/azure/sentinel/notebooks-hunt",
+            "redirect_document_id": true
+        },
         {
             "source_path": "articles/sentinel/detect-threats-built-in.md#use-analytics-rule-templates",
             "redirect_url": "/azure/sentinel/create-analytics-rule-from-template",

CODEOWNERS

@@ -15,3 +15,7 @@
 /articles/mariadb @tynevi @thomps23
 /articles/mysql @tynevi @thomps23
 /articles/managed-instance-apache-cassandra @tynevi @thomps23
+/articles/virtual-machines @jasonwhowell @thomps23
+/articles/virtual-machines-scale-set @jasonwhowell @thomps23
+/articles/container-instances @jasonwhowell @thomps23
+/articles/service-fabric @jasonwhowell @thomps23

articles/ai-services/computer-vision/how-to/specify-detection-model.md

@@ -37,13 +37,19 @@ You should be familiar with the concept of AI face detection. If you aren't, see
 
 The different face detection models are optimized for different tasks. See the following table for an overview of the differences.
 
-
-| Model | Description | Performance notes   | Attributes  | Landmarks |
-|---------|------------|-------------------|-------------|--|
-|**detection_01** | Default choice for all face detection operations. | Not optimized for small, side-view, or blurry faces.                                                     | Returns main face attributes (head pose, glasses, and so on) if they're specified in the detect call. | Returns face landmarks if they're specified in the detect call. |
-|**detection_02** | Released in May 2019 and available optionally in all face detection operations.   | Improved accuracy on small, side-view, and blurry faces.  | Doesn't return face attributes. | Doesn't return face landmarks.     |
-|**detection_03** | Released in February 2021 and available optionally in all face detection operations.                   | Further improved accuracy, including on smaller faces (64x64 pixels) and rotated face orientations.       | Returns mask, blur, and head pose attributes if they're specified in the detect call. | Returns face landmarks if they're specified in the detect call. |
-
+| Model | Description | Performance notes | Landmarks |
+|-------|-------------|-------------------|-----------|
+|**detection_01** | Default choice for all face detection operations. | Not optimized for small, side-view, or blurry faces. | Returns face landmarks if they're specified in the detect call. |
+|**detection_02** | Released in May 2019 and available optionally in all face detection operations. | Improved accuracy on small, side-view, and blurry faces. | Doesn't return face landmarks. |
+|**detection_03** | Released in February 2021 and available optionally in all face detection operations. | Further improved accuracy, including on smaller faces (64x64 pixels) and rotated face orientations. | Returns face landmarks if they're specified in the detect call. |
+
+Attributes are a set of features that can optionally be detected if they're specified in the detect call:
+
+| Model | accessories | blur | exposure | glasses | headPose | mask | noise | occlusion | qualityForRecognition |
+|-------|:-----------:|:----:|:--------:|:-------:|:--------:|:----:|:-----:|:---------:|:---------------------:|
+|**detection_01** | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | | ✔️ | ✔️ | ✔️ (for recognition_03 or 04) |
+|**detection_02** | | | | | | | | | |
+|**detection_03** | | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | | ✔️ | ✔️ (for recognition_03 or 04) |
 
 The best way to compare the performances of the detection models is to use them on a sample dataset. We recommend calling the [Detect] API on a variety of images, especially images of many faces or of faces that are difficult to see, using each detection model. Pay attention to the number of faces that each model returns.
 

articles/ai-services/computer-vision/whats-new.md

@@ -18,6 +18,18 @@ ms.author: pafarley
 
 Learn what's new in Azure AI Vision. Check this page to stay up to date with new features, enhancements, fixes, and documentation updates. 
 
+## August 2024
+
+### New detectable Face attributes
+
+The glasses, occlusion, blur, and exposure attributes are available with the latest Detection 03 model. See [Specify a face detection model](./how-to/specify-detection-model.md) for more details.
+
+## May 2024
+
+### New Face SDK 1.0.0-beta.1 (breaking changes)
+
+The Face SDK was rewritten in version 1.0.0-beta.1 to better meet the guidelines and design principles of Azure SDKs. C#, Python, Java, and JavaScript are the supported languages. Follow the [QuickStart](./quickstarts-sdk/identity-client-library.md) to get started.
+
 ## February 2024
 
 #### Multimodal embeddings GA: new multi-language model

articles/ai-studio/how-to/deploy-models-cohere-command.md

@@ -16,6 +16,8 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Cohere Command chat models
 
+[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+
 In this article, you learn about Cohere Command chat models and how to use them.
 The Cohere family of models includes various models optimized for different use cases, including chat completions, embeddings, and rerank. Cohere models are optimized for various use cases that include reasoning, summarization, and question answering.
 

articles/ai-studio/how-to/deploy-models-cohere-embed.md

@@ -16,6 +16,8 @@ zone_pivot_groups: azure-ai-model-catalog-samples-embeddings
 
 # How to use Cohere Embed V3 models with Azure AI Studio
 
+[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+
 In this article, you learn about Cohere Embed V3 models and how to use them with Azure AI Studio.
 The Cohere family of models includes various models optimized for different use cases, including chat completions, embeddings, and rerank. Cohere models are optimized for various use cases that include reasoning, summarization, and question answering.
 

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

@@ -16,6 +16,8 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Jais chat models
 
+[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+
 In this article, you learn about Jais chat models and how to use them.
 JAIS 30b Chat is an autoregressive bi-lingual LLM for **Arabic** & **English**. The tuned versions use supervised fine-tuning (SFT). The model is fine-tuned with both Arabic and English prompt-response pairs. The fine-tuning datasets included a wide range of instructional data across various domains. The model covers a wide range of common tasks including question answering, code generation, and reasoning over textual content. To enhance performance in Arabic, the Core42 team developed an in-house Arabic dataset and translated some open-source English instructions into Arabic.
 

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

@@ -16,6 +16,8 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Jamba-Instruct chat models
 
+[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+
 In this article, you learn about Jamba-Instruct chat models and how to use them.
 The Jamba-Instruct model is AI21's production-grade Mamba-based large language model (LLM) which uses AI21's hybrid Mamba-Transformer architecture. It's an instruction-tuned version of AI21's hybrid structured state space model (SSM) transformer Jamba model. The Jamba-Instruct model is built for reliable commercial use with respect to quality and performance.
 

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

@@ -16,6 +16,8 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Meta Llama chat models
 
+[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+
 In this article, you learn about Meta Llama chat models and how to use them.
 Meta Llama 2 and 3 models and tools are a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. The model family also includes fine-tuned versions optimized for dialogue use cases with reinforcement learning from human feedback (RLHF).
 

articles/ai-studio/how-to/deploy-models-mistral-nemo.md

@@ -16,6 +16,8 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 
 # How to use Mistral Nemo chat model
 
+[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
+
 In this article, you learn about Mistral Nemo chat model and how to use them.
 Mistral AI offers two categories of models. Premium models including [Mistral Large and Mistral Small](deploy-models-mistral.md), available as serverless APIs with pay-as-you-go token-based billing. Open models including [Mistral Nemo](deploy-models-mistral-nemo.md), [Mixtral-8x7B-Instruct-v01, Mixtral-8x7B-v01, Mistral-7B-Instruct-v01, and Mistral-7B-v01](deploy-models-mistral-open.md); available to also download and run on self-hosted managed endpoints.