Merge pull request #3652 from eric-urban/eur/ai-foundry-links

AI Foundry links

Author: prmerger-automator[bot]

articles/ai-services/openai/concepts/provisioned-throughput.md

@@ -46,7 +46,7 @@ An Azure OpenAI Deployment is a unit of management for a specific OpenAI Model.
 ## How much throughput per PTU you get for each model
 The amount of throughput (tokens per minute or TPM) a deployment gets per PTU is a function of the input and output tokens in the minute. Generating output tokens requires more processing than input tokens. For the models specified in the table below, 1 output token counts as 3 input tokens towards your TPM per PTU limit. The service dynamically balances the input & output costs, so users do not have to set specific input and output limits. This approach means your deployment is resilient to fluctuations in the workload shape.
 
-To help with simplifying the sizing effort, the following table outlines the TPM per PTU for the specified models. To understand the impact of output tokens on the TPM per PTU limit, use the 3 input token to 1 output token ratio. For a detailed understanding of how different ratios of input and output tokens impact the throughput your workload needs, see the [Azure OpenAI capacity calculator](https://oai.azure.com/portal/calculator). The table also shows Service Level Agreement (SLA) Latency Target Values per model.  For more information about the SLA for Azure OpenAI Service, see the [Service Level Agreements (SLA) for Online Services page](https://www.microsoft.com/licensing/docs/view/Service-Level-Agreements-SLA-for-Online-Services?lang=1)
+To help with simplifying the sizing effort, the following table outlines the TPM per PTU for the specified models. To understand the impact of output tokens on the TPM per PTU limit, use the 3 input token to 1 output token ratio. For a detailed understanding of how different ratios of input and output tokens impact the throughput your workload needs, see the [Azure OpenAI capacity calculator](https://ai.azure.com/resource/calculator). The table also shows Service Level Agreement (SLA) Latency Target Values per model.  For more information about the SLA for Azure OpenAI Service, see the [Service Level Agreements (SLA) for Online Services page](https://www.microsoft.com/licensing/docs/view/Service-Level-Agreements-SLA-for-Online-Services?lang=1)
 
 |Topic| **gpt-4o**   | **gpt-4o-mini**  | **o1**|
 | --- | --- | --- | --- |
@@ -57,7 +57,7 @@ To help with simplifying the sizing effort, the following table outlines the TPM
 |Input TPM per PTU |2,500|37,000|230|
 |Latency Target Value |25 Tokens Per Second|33 Tokens Per Second|25 Tokens Per Second|
 
-For a full list see the [Azure OpenAI Service in Azure AI Foundry portal calculator](https://oai.azure.com/portal/calculator).
+For a full list see the [Azure OpenAI Service in Azure AI Foundry portal calculator](https://ai.azure.com/resource/calculator).
 
 
 > [!NOTE]
@@ -135,7 +135,7 @@ If an acceptable region isn't available to support the desire model, version and
 
 ### Determining the number of PTUs needed for a workload
 
-PTUs represent an amount of model processing capacity. Similar to your computer or databases, different workloads or requests to the model will consume different amounts of underlying processing capacity. The conversion from throughput needs to PTUs can be approximated using historical token usage data or call shape estimations (input tokens, output tokens, and requests per minute) as outlined in our [performance and latency](../how-to/latency.md) documentation. To simplify this process, you can use the [Azure OpenAI Capacity calculator](https://oai.azure.com/portal/calculator) to size specific workload shapes.
+PTUs represent an amount of model processing capacity. Similar to your computer or databases, different workloads or requests to the model will consume different amounts of underlying processing capacity. The conversion from throughput needs to PTUs can be approximated using historical token usage data or call shape estimations (input tokens, output tokens, and requests per minute) as outlined in our [performance and latency](../how-to/latency.md) documentation. To simplify this process, you can use the [Azure OpenAI Capacity calculator](https://ai.azure.com/resource/calculator) to size specific workload shapes.
 
 A few high-level considerations:
 - Generations require more capacity than prompts
@@ -187,7 +187,7 @@ For provisioned deployments, we use a variation of the leaky bucket algorithm to
 
 #### How many concurrent calls can I have on my deployment?
 
-The number of concurrent calls you can achieve depends on each call's shape (prompt size, `max_tokens` parameter, etc.). The service continues to accept calls until the utilization reaches 100%. To determine the approximate number of concurrent calls, you can model out the maximum requests per minute for a particular call shape in the [capacity calculator](https://oai.azure.com/portal/calculator). If the system generates less than the number of output tokens set for the `max_tokens` parameter, then the provisioned deployment will accept more requests.
+The number of concurrent calls you can achieve depends on each call's shape (prompt size, `max_tokens` parameter, etc.). The service continues to accept calls until the utilization reaches 100%. To determine the approximate number of concurrent calls, you can model out the maximum requests per minute for a particular call shape in the [capacity calculator](https://ai.azure.com/resource/calculator). If the system generates less than the number of output tokens set for the `max_tokens` parameter, then the provisioned deployment will accept more requests.
 
 ## What models and regions are available for provisioned throughput?
 

articles/ai-services/openai/concepts/use-your-data.md

@@ -19,7 +19,7 @@ Use this article to learn about Azure OpenAI On Your Data, which makes it easier
 
 ## What is Azure OpenAI On Your Data
 
-Azure OpenAI On Your Data enables you to run advanced AI models such as GPT-35-Turbo and GPT-4 on your own enterprise data without needing to train or fine-tune models. You can chat on top of and analyze your data with greater accuracy. You can specify sources to support the responses based on the latest information available in your designated data sources. You can access Azure OpenAI On Your Data using a REST API, via the SDK or the web-based interface in the [Azure AI Foundry portal](https://oai.azure.com/). You can also create a web app that connects to your data to enable an enhanced chat solution or deploy it directly as a copilot in the Copilot Studio (preview).
+Azure OpenAI On Your Data enables you to run advanced AI models such as GPT-35-Turbo and GPT-4 on your own enterprise data without needing to train or fine-tune models. You can chat on top of and analyze your data with greater accuracy. You can specify sources to support the responses based on the latest information available in your designated data sources. You can access Azure OpenAI On Your Data using a REST API, via the SDK or the web-based interface in the [Azure AI Foundry portal](https://ai.azure.com/). You can also create a web app that connects to your data to enable an enhanced chat solution or deploy it directly as a copilot in the Copilot Studio (preview).
 
 ## Developing with Azure OpenAI On Your Data
 

articles/ai-services/openai/how-to/fine-tuning-deploy.md

@@ -306,7 +306,7 @@ az cognitiveservices account deployment create
 
 ## [Portal](#tab/portal)
 
-After your custom model deploys, you can use it like any other deployed model. You can use the **Playgrounds** in [Azure AI Foundry portal](https://oai.azure.com) to experiment with your new deployment. You can continue to use the same parameters with your custom model, such as `temperature` and `max_tokens`, as you can with other deployed models.
+After your custom model deploys, you can use it like any other deployed model. You can use the **Playgrounds** in the [Azure AI Foundry portal](https://ai.azure.com) to experiment with your new deployment. You can continue to use the same parameters with your custom model, such as `temperature` and `max_tokens`, as you can with other deployed models.
 
 :::image type="content" source="../media/quickstarts/playground-load-new.png" alt-text="Screenshot of the Playground pane in Azure AI Foundry portal, with sections highlighted." lightbox="../media/quickstarts/playground-load-new.png":::
 

articles/ai-services/openai/how-to/on-your-data-configuration.md

@@ -59,7 +59,7 @@ Azure OpenAI On Your Data lets you restrict the documents that can be used in re
     `group_ids` is the default field name. If you use a different field name like `my_group_ids`, you can map the field in [index field mapping](../concepts/use-your-data.md#index-field-mapping).
 
 1. Make sure each sensitive document in the index has this security field value set to the permitted groups of the document.
-1. In [Azure AI Foundry portal](https://oai.azure.com/portal), add your data source. in the [index field mapping](../concepts/use-your-data.md#index-field-mapping) section, you can map zero or one value to the **permitted groups** field, as long as the schema is compatible. If the **permitted groups** field isn't mapped, document level access is disabled. 
+1. In the [Azure AI Foundry portal](https://ai.azure.com/portal), add your data source. In the [index field mapping](../concepts/use-your-data.md#index-field-mapping) section, you can map zero or one value to the **permitted groups** field, as long as the schema is compatible. If the **permitted groups** field isn't mapped, document level access is disabled. 
 
 **Azure AI Foundry portal**
 

articles/ai-services/openai/how-to/stored-completions.md

@@ -249,7 +249,7 @@ curl $AZURE_OPENAI_ENDPOINT/openai/deployments/gpt-4o/chat/completions?api-versi
 
 ---
 
-Once stored completions are enabled for an Azure OpenAI deployment, they'll begin to show up in the [Azure AI Foundry portal](https://oai.azure.com) in the **Stored Completions** pane.
+Once stored completions are enabled for an Azure OpenAI deployment, they'll begin to show up in the [Azure AI Foundry portal](https://ai.azure.com) in the **Stored Completions** pane.
 
 :::image type="content" source="../media/stored-completions/stored-completions.png" alt-text="Screenshot of the stored completions User Experience." lightbox="../media/stored-completions/stored-completions.png":::
 
@@ -259,7 +259,7 @@ Distillation allows you to turn your stored completions into a fine-tuning datas
 
 Distillation requires a minimum of 10 stored completions, though it's recommended to provide hundreds to thousands of stored completions for the best results.
 
-1. From the **Stored Completions** pane in the [Azure AI Foundry portal](https://oai.azure.com) use the **Filter** options to select the completions you want to train your model with.
+1. From the **Stored Completions** pane in the [Azure AI Foundry portal](https://ai.azure.com) use the **Filter** options to select the completions you want to train your model with.
 
 2. To begin distillation, select **Distill**
 
@@ -288,7 +288,7 @@ The [evaluation](./evaluations.md) of large language models is a critical step i
 
 Stored completions can be used as a dataset for running evaluations.
 
-1. From the **Stored Completions** pane in the [Azure AI Foundry portal](https://oai.azure.com) use the **Filter** options to select the completions you want to be part of your evaluation dataset.
+1. From the **Stored Completions** pane in the [Azure AI Foundry portal](https://ai.azure.com) use the **Filter** options to select the completions you want to be part of your evaluation dataset.
 
 2. To configure the evaluation, select **Evaluate**
 

articles/ai-services/openai/includes/chatgpt-studio.md

@@ -16,7 +16,7 @@ ms.date: 09/19/2024
 
 ## Go to Azure AI Foundry
 
-Navigate to [Azure AI Foundry](https://oai.azure.com/) and sign-in with credentials that have access to your Azure OpenAI resource. During or after the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
+Navigate to the [Azure AI Foundry portal](https://ai.azure.com/) and sign-in with credentials that have access to your Azure OpenAI resource. During or after the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
 
 From Azure AI Foundry, select **Chat playground**.
 

articles/ai-services/openai/includes/fine-tuning-studio.md

@@ -101,7 +101,7 @@ In general, doubling the dataset size can lead to a linear increase in model qua
 
 Azure AI Foundry portal provides the **Create custom model** wizard, so you can interactively create and train a fine-tuned model for your Azure resource.
 
-1. Open Azure AI Foundry portal at <a href="https://oai.azure.com/" target="_blank">https://oai.azure.com/</a> and sign in with credentials that have access to your Azure OpenAI resource. During the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
+1. Go to the Azure AI Foundry portal at <a href="https://ai.azure.com/" target="_blank">https://ai.azure.com/</a> and sign in with credentials that have access to your Azure OpenAI resource. During the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
 
 1. In Azure AI Foundry portal, browse to the **Tools > Fine-tuning** pane, and select **Fine-tune model**.
 

articles/ai-services/openai/includes/fine-tuning-unified.md

@@ -14,9 +14,9 @@ ms.author: mbullwin
 There are two unique fine-tuning experiences in the Azure AI Foundry portal:
 
 * [Hub/Project view](https://ai.azure.com) - supports fine-tuning models from multiple providers including Azure OpenAI, Meta Llama, Microsoft Phi, etc.
-* [Azure OpenAI centric view](https://oai.azure.com) - only supports fine-tuning Azure OpenAI models, but has support for additional features like the [Weights & Biases (W&B) preview integration](../how-to/weights-and-biases-integration.md). 
+* [Azure OpenAI centric view](https://ai.azure.com/resource/overview) - only supports fine-tuning Azure OpenAI models, but has support for additional features like the [Weights & Biases (W&B) preview integration](../how-to/weights-and-biases-integration.md). 
 
-If you are only fine-tuning Azure OpenAI models, we recommend the Azure OpenAI centric fine-tuning experience which is available by navigating to [https://oai.azure.com](https://oai.azure.com). 
+If you are only fine-tuning Azure OpenAI models, we recommend the Azure OpenAI centric fine-tuning experience which is available by navigating to [https://ai.azure.com/resource/overview](https://ai.azure.com/resource/overview). 
 
 # [Azure OpenAI](#tab/azure-openai)
 

articles/ai-services/openai/includes/studio.md

@@ -15,7 +15,7 @@ ms.date: 09/19/2023
 
 ## Go to the Azure AI Foundry
 
-Navigate to <a href="https://oai.azure.com/" target="_blank">Azure AI Foundry</a> and sign-in with credentials that have access to your Azure OpenAI resource. During or after the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
+Navigate to the <a href="https://ai.azure.com/" target="_blank">Azure AI Foundry portal</a> and sign-in with credentials that have access to your Azure OpenAI resource. During or after the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
 
 ## Playground
 
@@ -39,7 +39,7 @@ In the Completions playground you can also view Python and curl code samples pre
 
 To use the Azure OpenAI for text summarization in the Completions playground, follow these steps:
 
-1. Sign in to [Azure AI Foundry](https://oai.azure.com).
+1. Sign in to the [Azure AI Foundry portal](https://ai.azure.com).
 1. Select the subscription and OpenAI resource to work with. 
 1. Select **Completions playground** on the landing page.
 1. Select your deployment from the **Deployments** dropdown. If your resource doesn't have a deployment, select **Create a deployment** and then revisit this step.