Merge pull request #281540 from MicrosoftDocs/main

[Out of Band Publish] 7/23/2024 08:15 AM Publish

Author: Albertyang0

articles/ai-services/speech-service/includes/language-support/multilingual-voices.md

@@ -100,6 +100,7 @@ There isn't a single right answer to this question, but you should have clearly
 Now that you know when to leverage fine-tuning for your use-case, you can go to Azure AI Studio to find several models available to fine-tune including:
 - Azure OpenAI models
 - Llama 2 family models
+- Llama 3.1 family of models
 
 
 ### Azure OpenAI models
@@ -126,6 +127,13 @@ The following Llama 2 family models are supported in Azure AI Studio for fine-tu
 
 Fine-tuning of Llama 2 models is currently supported in projects located in West US 3.
 
+### Llama 3.1 family models
+The following Llama 3.1 family models are supported in Azure AI Studio for fine-tuning:
+- `Llama-3.1-70b`
+- `Llama-3.1-7b`
+
+Fine-tuning of Llama 3.1 models is currently supported in projects located in West US 3.
+
 ## Related content
 
 - [Learn how to fine-tune an Azure OpenAI model in Azure AI Studio](../../ai-services/openai/how-to/fine-tuning.md?context=/azure/ai-studio/context/context)

articles/ai-studio/concepts/fine-tuning-overview.md

@@ -5,7 +5,7 @@ description: Learn how to fine-tune Meta Llama models in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio
 ms.topic: how-to
-ms.date: 5/21/2024
+ms.date: 7/18/2024
 ms.reviewer: rasavage
 reviewer: shubhirajMsft
 ms.author: ssalgado
@@ -23,12 +23,17 @@ Fine-tuning provides significant value by enabling customization and optimizatio
 
 In this article, you learn how to fine-tune Meta Llama models in [Azure AI Studio](https://ai.azure.com).
 
-The [Meta Llama family of large language models (LLMs)](./deploy-models-llama.md) is 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), called Llama-2-chat.
+The [Meta Llama family of large language models (LLMs)](./deploy-models-llama.md) is 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), called Llama-Instruct.
 
 ## Models
-# [Meta Llama 3](#tab/llama-three)
+# [Meta Llama 3.1](#tab/llama-three)
 
-Fine-tuning of Llama 3 models is currently not supported.
+The following models are available in Azure Marketplace for Llama 3.1 when fine-tuning as a service with pay-as-you-go billing:
+
+- `Llama-3.1-80B-Instruct` (preview)
+- `LLama-3.1-8b-Instruct` (preview)
+![IMPORTANT]
+> At this time we can not do fine-tuning for Llama 3.1 with context length of 128K. The current context length is limited to 65k.
 
 # [Meta Llama 2](#tab/llama-two)
 
@@ -44,9 +49,35 @@ Fine-tuning of Llama 2 models is currently supported in projects located in West
 
 ## Prerequisites
 
-# [Meta Llama 3](#tab/llama-three)
+# [Meta Llama 3.1](#tab/llama-three)
+
+
+ An Azure subscription with a valid payment method. Free or trial Azure subscriptions won't work. If you don't have an Azure subscription, create a [paid Azure account](https://azure.microsoft.com/pricing/purchase-options/pay-as-you-go) to begin.
+- An [Azure AI hub resource](../how-to/create-azure-ai-resource.md).
+
+    > [!IMPORTANT]
+    > For Meta Llama 3.1 models, the pay-as-you-go model fine-tune offering is only available with AI hubs created in **West US 3** regions.
+
+- An [Azure AI project](../how-to/create-projects.md) in Azure AI Studio.
+- Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Studio. To perform the steps in this article, your user account must be assigned the __owner__ or __contributor__ role for the Azure subscription. Alternatively, your account can be assigned a custom role that has the following permissions:
+
+    - On the Azure subscription—to subscribe the Azure AI project to the Azure Marketplace offering, once for each project, per offering:
+      - `Microsoft.MarketplaceOrdering/agreements/offers/plans/read`
+      - `Microsoft.MarketplaceOrdering/agreements/offers/plans/sign/action`
+      - `Microsoft.MarketplaceOrdering/offerTypes/publishers/offers/plans/agreements/read`
+      - `Microsoft.Marketplace/offerTypes/publishers/offers/plans/agreements/read`
+      - `Microsoft.SaaS/register/action`
+ 
+    - On the resource group—to create and use the SaaS resource:
+      - `Microsoft.SaaS/resources/read`
+      - `Microsoft.SaaS/resources/write`
+ 
+    - On the Azure AI project—to deploy endpoints (the Azure AI Developer role contains these permissions already):
+      - `Microsoft.MachineLearningServices/workspaces/marketplaceModelSubscriptions/*`  
+      - `Microsoft.MachineLearningServices/workspaces/serverlessEndpoints/*`
+
+    For more information on permissions, see [Role-based access control in Azure AI Studio](../concepts/rbac-ai-studio.md).
 
-Fine-tuning of Llama 3 models is currently not supported.
 
 # [Meta Llama 2](#tab/llama-two)
 
@@ -139,9 +170,43 @@ The supported file type is JSON Lines. Files are uploaded to the default datasto
 
 ## Fine-tune a Meta Llama model
 
-# [Meta Llama 3](#tab/llama-three)
+# [Meta Llama 3.1](#tab/llama-three)
+
+To fine-tune a LLama 3.1 model:
+
+1. Sign in to [Azure AI Studio](https://ai.azure.com).
+1. Choose the model you want to fine-tune from the Azure AI Studio [model catalog](https://ai.azure.com/explore/models). 
+
+1. On the model's **Details** page, select **fine-tune**.
+
+1. Select the project in which you want to fine-tune your models. To use the pay-as-you-go model fine-tune offering, your workspace must belong to the **West US 3** region.
+1. On the fine-tune wizard, select the link to **Azure Marketplace Terms** to learn more about the terms of use. You can also select the **Marketplace offer details** tab to learn about pricing for the selected model.
+1. If this is your first time fine-tuning the model in the project, you have to subscribe your project for the particular offering (for example, Meta-Llama-3-70B) from Azure Marketplace. This step requires that your account has the Azure subscription permissions and resource group permissions listed in the prerequisites. Each project has its own subscription to the particular Azure Marketplace offering, which allows you to control and monitor spending. Select **Subscribe and fine-tune**.
+
+    > [!NOTE]
+    > Subscribing a project to a particular Azure Marketplace offering (in this case, Meta-Llama-3-70B) requires that your account has **Contributor** or **Owner** access at the subscription level where the project is created. Alternatively, your user account can be assigned a custom role that has the Azure subscription permissions and resource group permissions listed in the [prerequisites](#prerequisites).
+
+1. Once you sign up the project for the particular Azure Marketplace offering, subsequent fine-tuning of the _same_ offering in the _same_ project don't require subscribing again. Therefore, you don't need to have the subscription-level permissions for subsequent fine-tune jobs. If this scenario applies to you, select **Continue to fine-tune**.
+
+1. Enter a name for your fine-tuned model and the optional tags and description.
+1. Select training data to fine-tune your model. See [data preparation](#data-preparation) for more information.
+
+    > [!NOTE]
+    > If the you has their training/validation files in a credential less datastore, they will need to allow workspace managed identity access to their datastore in order to proceed with MaaS finetuning with a credential less storage. That would be this setting on the "Datastore" page, after clicking "Update authentication" > Select the following option: 
+	
+	![Use workspace managed identity for data preview and profiling in Azure Machine Learning Studio.](../media/how-to/fine-tune/llama/credentials.png)
+
+    Make sure all your training examples follow the expected format for inference. To fine-tune models effectively, ensure a balanced and diverse dataset. This involves maintaining data balance, including various scenarios, and periodically refining training data to align with real-world expectations, ultimately leading to more accurate and balanced model responses.
+    - The batch size to use for training. When set to -1, batch_size is calculated as 0.2% of examples in training set and the max is 256.
+    - The fine-tuning learning rate is the original learning rate used for pretraining multiplied by this multiplier. We recommend experimenting with values between 0.5 and 2. Empirically, we've found that larger learning rates often perform better with larger batch sizes. Must be between 0.0 and 5.0.
+    - Number of training epochs. An epoch refers to one full cycle through the data set.
+
+1. Task parameters are an optional step and an advanced option- Tuning hyperparameter is essential for optimizing large language models (LLMs) in real-world applications. It allows for improved performance and efficient resource usage. The default settings can be used or advanced users can customize parameters like epochs or learning rate.
+
+1. Review your selections and proceed to train your model.
+
+Once your model is fine-tuned, you can deploy the model and can use it in your own application, in the playground, or in prompt flow. For more information, see [How to deploy Llama 3.1 family of large language models with Azure AI Studio](./deploy-models-llama.md).
 
-Fine-tuning of Llama 3 models is currently not supported.
 
 # [Meta Llama 2](#tab/llama-two)
 

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

@@ -26,7 +26,7 @@ For information about installing ITSMC, see [Add the IT Service Management Conne
 
 ### OAuth setup
 
-ServiceNow supported versions include Vancouver, Utah, Tokyo, San Diego, Rome, Quebec,  Paris, Orlando, New York, Madrid, London, Kingston, Jakarta, Istanbul, Helsinki, and Geneva.
+ServiceNow supported versions include Washington, Vancouver, Utah, Tokyo, San Diego, Rome, Quebec,  Paris, Orlando, New York, Madrid, London, Kingston, Jakarta, Istanbul, Helsinki, and Geneva.
 
 ServiceNow admins must generate a client ID and client secret for their ServiceNow instance. See the following information as required:
 

articles/ai-studio/how-to/fine-tune-model-llama.md

@@ -58,8 +58,8 @@ This article provides onboarding guidance for the following types of clusters. A
 
 > [!NOTE]
 > The Managed Prometheus Arc-Enabled Kubernetes extension does not support the following configurations:
-> * Red Hat Openshift distributions
-  > * Windows nodes
+> * Red Hat Openshift distributions, including Azure Red Hat OpenShift (ARO)
+> * Windows nodes
 
 
 ## Workspaces

articles/ai-studio/media/how-to/fine-tune/llama/credentials.png

@@ -8,29 +8,29 @@ ms.custom: compute-evergreen
 tag: top-support-issue 
 ms.service: cloud-services
 ms.topic: troubleshooting
-ms.date: 02/21/2023
+ms.date: 07/23/2024
 ---
 
 # Troubleshooting applications that don't support TLS 1.2
 
 [!INCLUDE [Cloud Services (classic) deprecation announcement](includes/deprecation-announcement.md)]
 
-This article describes how to enable the older TLS protocols (TLS 1.0 and 1.1) as well as applying legacy cipher suites to support the additional protocols on the Windows Server 2019 cloud service web and worker roles.
+This article describes how to enable the older TLS protocols (TLS 1.0 and 1.1). It also covers the application of legacy cipher suites to support the additional protocols on the Windows Server 2019 cloud service web and worker roles.
 
-We understand that while we are taking steps to deprecate TLS 1.0 and TLS 1.1, our customers may need to support the older protocols and cipher suites until they can plan for their deprecation.  While we don't recommend re-enabling these legacy values, we are providing guidance to help customers. We encourage customers to evaluate the risk of regression before implementing the changes outlined in this article.
+We understand that while we're taking steps to deprecate TLS 1.0 and TLS 1.1, our customers may need to support the older protocols and cipher suites in the meantime. While we don't recommend re-enabling these legacy values, we're providing guidance to help customers. We encourage customers to evaluate the risk of regression before implementing the changes outlined in this article.
 
 > [!NOTE]
 > Guest OS Family 6 release enforces TLS 1.2 by explicitly disabling TLS 1.0 and 1.1 and defining a specific set of cipher suites.For more information on Guest OS families see [Guest OS release news](./cloud-services-guestos-update-matrix.md#family-6-releases)
 
 ## Dropping support for TLS 1.0, TLS 1.1 and older cipher suites
 
-In support of our commitment to use best-in-class encryption, Microsoft announced plans to start migration away from TLS 1.0 and 1.1 in June of 2017.   Since that initial announcement, Microsoft announced our intent to disable Transport Layer Security (TLS) 1.0 and 1.1 by default in supported versions of Microsoft Edge and Internet Explorer 11 in the first half of 2020.  Similar announcements from Apple, Google, and Mozilla indicate the direction in which the industry is headed.
+In support of our commitment to use best-in-class encryption, Microsoft announced plans to start migration away from TLS 1.0 and 1.1 in June of 2017. Microsoft announced our intent to disable Transport Layer Security (TLS) 1.0 and 1.1 by default in supported versions of Microsoft Edge and Internet Explorer 11 in the first half of 2020. Similar announcements from Apple, Google, and Mozilla indicate the direction in which the industry is headed.
 
 For more information, see [Preparing for TLS 1.2 in Microsoft Azure](https://azure.microsoft.com/updates/azuretls12/)
 
 ## TLS configuration
 
-The Windows Server 2019 cloud server image is configured with TLS 1.0 and TLS 1.1 disabled at the registry level. This means applications deployed to this version of Windows AND using the Windows stack for TLS negotiation will not allow TLS 1.0 and TLS 1.1 communication.
+The Windows Server 2019 cloud server image is configured with TLS 1.0 and TLS 1.1 disabled at the registry level. This means applications deployed to this version of Windows AND using the Windows stack for TLS negotiation won't allow TLS 1.0 and TLS 1.1 communication.
 
 The server also comes with a limited set of cipher suites:
 
@@ -47,7 +47,7 @@ The server also comes with a limited set of cipher suites:
 
 ## Step 1: Create the PowerShell script to enable TLS 1.0 and TLS 1.1
 
-Use the following code as an example to create a script that enables the older protocols and cipher suites. For the purposes of this documentation, this script will be named: **TLSsettings.ps1**. Store this script on your local desktop for easy access in later steps.
+Use the following code as an example to create a script that enables the older protocols and cipher suites. For the purposes of this documentation, this script is named: **TLSsettings.ps1**. Store this script on your local desktop for easy access in later steps.
 
 ```powershell
 # You can use the -SetCipherOrder (or -sco) option to also set the TLS cipher 
@@ -268,7 +268,7 @@ If ($reboot) {
 
 ## Step 2: Create a command file
 
-Create a CMD file named **RunTLSSettings.cmd** using the below. Store this script on your local desktop for easy access in later steps.
+Create a CMD file named **RunTLSSettings.cmd** using the following script. Store this script on your local desktop for easy access in later steps.
 
 ```cmd
 SET LOG_FILE="%TEMP%\StartupLog.txt"
@@ -303,7 +303,7 @@ Add the following snippet to your existing service definition file.
 	</Startup> 
 ```
 
-Here is an example that shows both the worker role and web role.
+Here's an example that shows both the worker role and web role.
 
 ```
 <?xmlversion="1.0" encoding="utf-8"?> 
@@ -352,6 +352,6 @@ To ensure the scripts are uploaded with every update pushed from Visual Studio,
 
 ## Step 6: Publish & Validate
 
-Now that the above steps have been complete, publish the update to your existing Cloud Service.
+Now that you completed the previous steps, publish the update to your existing Cloud Service.
 
 You can use [SSLLabs](https://www.ssllabs.com/) to validate the TLS status of your endpoints

articles/azure-monitor/alerts/itsmc-connections-servicenow.md

@@ -3,7 +3,7 @@ title: Manage Azure Cloud Services (classic) using Azure Automation | Microsoft
 description: Learn about how the Azure Automation service can be used to manage Azure cloud services at scale.
 ms.topic: article
 ms.service: cloud-services
-ms.date: 02/21/2023
+ms.date: 07/23/2024
 author: hirenshah1
 ms.author: hirshah
 ms.reviewer: mimckitt
@@ -16,17 +16,17 @@ ms.custom: compute-evergreen
 This guide introduces you to the Azure Automation service, and how it can be used to simplify management of your Azure cloud services.
 
 ## What is Azure Automation?
-[Azure Automation](https://azure.microsoft.com/services/automation/) is an Azure service for simplifying cloud management through process automation. Using Azure Automation, long-running, manual, error-prone, and frequently repeated tasks can be automated to increase reliability, efficiency, and time to value for your organization.
+[Azure Automation](https://azure.microsoft.com/services/automation/) is an Azure service for simplifying cloud management through process automation. When you use Azure Automation, you can automate long-running, manual, error-prone, and frequently repeated tasks to increase reliability, efficiency, and time to value for your organization.
 
-Azure Automation provides a highly reliable and highly available workflow execution engine that scales to meet your needs as your organization grows. In Azure Automation, processes can be kicked off manually, by third-party systems, or at scheduled intervals so that tasks happen exactly when needed.
+Azure Automation provides a highly reliable and highly available workflow execution engine that scales to meet your needs as your organization grows. In Azure Automation, processes can be kicked off manually, by non-Microsoft systems, or at scheduled intervals so that tasks happen exactly when needed.
 
-Lower operational overhead and free up IT / DevOps staff to focus on work that adds business value by moving your cloud management tasks to be run automatically by Azure Automation.
+Lower operational overhead and free up IT / DevOps staff to focus on work that adds business value by running your cloud management tasks automatically with Azure Automation.
 
 ## How can Azure Automation help manage Azure cloud services?
-Azure cloud services can be managed in Azure Automation by using the PowerShell cmdlets that are available in the [Azure PowerShell tools](/powershell/). Azure Automation has these cloud service PowerShell cmdlets available out of the box, so that you can perform all of your cloud service management tasks within the service. You can also pair these cmdlets in Azure Automation with the cmdlets for other Azure services, to automate complex tasks across Azure services and third party systems.
+Azure cloud services can be managed in Azure Automation by using the PowerShell cmdlets that are available in the [Azure PowerShell tools](/powershell/). Azure Automation has these cloud service PowerShell cmdlets available out of the box, so that you can perform all of your cloud service management tasks within the service. You can also pair these cmdlets in Azure Automation with the cmdlets for other Azure services, to automate complex tasks across Azure services and non-Microsoft systems.
 
 ## Next Steps
-Now that you've learned the basics of Azure Automation and how it can be used to manage Azure cloud services, follow these links to learn more about Azure Automation.
+Now that you covered the basics of Azure Automation and how it can be used to manage Azure cloud services, follow these links to learn more about Azure Automation.
 
 * [Azure Automation Overview](../automation/automation-intro.md)
 * [My first runbook](../automation/learn/powershell-runbook-managed-identity.md)