heading levels

Author: msakande

articles/ai-studio/concepts/model-lifecycle-and-retirement.md

@@ -17,6 +17,14 @@ reviewer: ManojBableshwar
 
 Models in the model catalog are continually refreshed with newer and more capable models. As part of this process, model providers might deprecate and retire their older models, and you might need to update your applications to use a newer model. This document communicates information about the model lifecycle and deprecation timelines and explains how you're informed of model lifecycle stages.
 
+> [!IMPORTANT]
+> This article describes deprecation and retirement only for models that can be deployed to __serverless APIs__, not managed compute. To learn more about the differences between deployment to serverless APIs and managed computes, see [Model catalog and collections in Azure AI Foundry portal](../how-to/model-catalog-overview.md).
+
+> [!NOTE]
+> Azure OpenAI models in the model catalog are provided through Azure OpenAI Service. For information about Azure Open AI model deprecation and retirement, see the [Azure OpenAI service product documentation](/azure/ai-services/openai/concepts/model-retirements).
+
+## Model lifecycle stages
+
 Models in the model catalog belong to one of these stages:
 
 - Preview
@@ -25,30 +33,23 @@ Models in the model catalog belong to one of these stages:
 - Deprecated
 - Retired
 
-> [!IMPORTANT]
-> This article describes deprecation and retirement only for models that can be deployed to __serverless APIs__, not managed compute. To learn more about the differences between deployment to serverless APIs and managed computes, see [Model catalog and collections in Azure AI Foundry portal](../how-to/model-catalog-overview.md).
-
-> [!NOTE]
-> Azure OpenAI models in the model catalog are provided through Azure OpenAI Service. For information about Azure Open AI model deprecation and retirement, see the [Azure OpenAI service product documentation](/azure/ai-services/openai/concepts/model-retirements).
-
-
-## Preview 
+### Preview
 
 Models labeled _Preview_ are experimental in nature. A model's weights, runtime, and API schema can change while the model is in preview. Models in preview aren't guaranteed to become generally available. Models in preview have a _Preview_ label next to their name in the model catalog.  
 
-## Generally available
+### Generally available
 
 This stage is the default model stage. Models that don't include a lifecycle label next to their name are generally available and suitable for use in production environments. In this stage, model weights and APIs are fixed. However, model containers or runtimes with vulnerabilities might get patched, but patches won't affect model outputs.  
 
-## Legacy
+### Legacy
 
 Models labeled _Legacy_ are intended for deprecation. You should plan to move to a different model, such as a new, improved model that might be available in the same model family. While a model is in the legacy stage, existing deployments of the model continue to work, and you can create new deployments of the model until the deprecation date.
 
-## Deprecated
+### Deprecated
 
 Models labeled _Deprecated_ are no longer available for new deployments. You can't create any new deployments for the model; however, existing deployments continue to work until the retirement date.
 
-## Retired
+### Retired
 
 Models labeled _Retired_ are no longer available for use. You can't create new deployments, and attempts to use existing deployments return `<return code>` errors.