Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into ingress-legacy

Author: asudbring

articles/ai-services/openai/concepts/legacy-models.md

@@ -1,20 +1,22 @@
 ---
-title: Azure OpenAI Service legacy models
+title: Azure OpenAI Service deprecated models
 titleSuffix: Azure OpenAI
-description: Learn about the legacy models in Azure OpenAI.
+description: Learn about the deprecated models in Azure OpenAI.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 07/06/2023
+ms.date: 02/26/2024
 ms.custom: references_regions, build-2023, build-2023-dataai
 manager: nitinme
-author: mrbullwinkle #ChrisHMSFT
-ms.author: mbullwin #chrhoder
+author: mrbullwinkle 
+ms.author: mbullwin 
 recommendations: false
 ---
 
-# Azure OpenAI Service legacy models
+# Azure OpenAI Service deprecated models
 
-Azure OpenAI Service offers a variety of models for different use cases. The following models are not available for new deployments beginning July 6, 2023. Deployments created prior to July 6, 2023 remain available to customers until July 5, 2024. We recommend customers migrate to the replacement models prior to the July 5, 2024 retirement.
+Azure OpenAI Service offers a variety of models for different use cases. The following models were deprecated on July 6, 2023 and will be retired on July 5, 2024.  These models are no longer available for new deployments. Deployments created prior to July 6, 2023 remain available to customers until July 5, 2024. We recommend customers migrate their applications to deployments of replacement models prior to the July 5, 2024 retirement.
+
+At the time of retirement, deployments of these models will stop returning valid API responses.
 
 ## GPT-3.5
 

articles/ai-services/openai/concepts/model-versions.md

@@ -23,7 +23,7 @@ We want to make it easy for customers to stay up to date as models improve.  Cus
 
 When a customer deploys GPT-3.5-Turbo and GPT-4 on Azure OpenAI Service, the standard behavior is to deploy the current default version – for example, GPT-4 version 0314.  When the default version changes to say GPT-4 version 0613, the deployment is automatically updated to version 0613 so that customer deployments feature the latest capabilities of the model.
 
-Customers can also deploy a specific version like GPT-4 0314 or GPT-4 0613 and choose an update policy, which can include the following options:
+Customers can also deploy a specific version like GPT-4 0613 and choose an update policy, which can include the following options:
 
 * Deployments set to **Auto-update to default** automatically update to use the new default version.
 * Deployments set to **Upgrade when expired** automatically update when its current version is retired.

articles/ai-services/openai/concepts/models.md

@@ -27,11 +27,12 @@ Azure OpenAI Service is powered by a diverse set of models with different capabi
 
 ## GPT-4 and GPT-4 Turbo Preview
 
- GPT-4 can solve difficult problems with greater accuracy than any of OpenAI's previous models. Like GPT-3.5 Turbo, GPT-4 is optimized for chat and works well for traditional completions tasks. Use the Chat Completions API to use GPT-4. To learn more about how to interact with GPT-4 and the Chat Completions API check out our [in-depth how-to](../how-to/chatgpt.md).
+ GPT-4 is a large multimodal model (accepting text or image inputs and generating text) that can solve difficult problems with greater accuracy than any of OpenAI's previous models. Like GPT-3.5 Turbo, GPT-4 is optimized for chat and works well for traditional completions tasks. Use the Chat Completions API to use GPT-4. To learn more about how to interact with GPT-4 and the Chat Completions API check out our [in-depth how-to](../how-to/chatgpt.md).
+
+ GPT-4 Turbo with Vision is the version of GPT-4 that accepts image inputs.  It is available as the `vision-preview` model of `gpt-4`.
 
 - `gpt-4`
 - `gpt-4-32k`
-- `gpt-4-vision`
 
 You can see the token context length supported by each model in the [model summary table](#model-summary-table-and-region-availability).
 
@@ -97,7 +98,6 @@ See [model versions](../concepts/model-versions.md) to learn about how Azure Ope
 > [!NOTE]
 > Version `0314` of `gpt-4` and `gpt-4-32k` will be retired no earlier than July 5, 2024.  Version `0613` of `gpt-4` and `gpt-4-32k` will be retired no earlier than September 30, 2024.  See [model updates](../how-to/working-with-models.md#model-updates) for model upgrade behavior.
 
-
 GPT-4 version 0125-preview is an updated version of the GPT-4 Turbo preview previously released as version 1106-preview.  GPT-4 version 0125-preview completes tasks such as code generation more completely compared to gpt-4-1106-preview.  Because of this, depending on the task, customers may find that GPT-4-0125-preview generates more output compared to the gpt-4-1106-preview.  We recommend customers compare the outputs of the new model.  GPT-4-0125-preview also addresses bugs in gpt-4-1106-preview with UTF-8 handling for non-English languages. 
 
 > [!IMPORTANT]
@@ -205,7 +205,7 @@ The following Embeddings models are available with [Azure Government](/azure/azu
 |  Model ID  | Feature Availability | Max Request (characters) |
 |  --- |  --- | :---: |
 | dalle2 | East US | 1,000 |
-| dalle3 | Sweden Central | 4,000 |
+| dall-e-3 | Sweden Central | 4,000 |
 
 ### Fine-tuning models
 

articles/ai-services/openai/how-to/working-with-models.md

@@ -20,31 +20,31 @@ You can get a list of models that are available for both inference and fine-tuni
 
 ## Model updates
 
-Azure OpenAI now supports automatic updates for select model deployments. On models where automatic update support is available, a model version drop-down will be visible in Azure OpenAI Studio under **Create new deployment** and **Edit deployment**:
+Azure OpenAI now supports automatic updates for select model deployments. On models where automatic update support is available, a model version drop-down is visible in Azure OpenAI Studio under **Create new deployment** and **Edit deployment**:
 
 :::image type="content" source="../media/models/auto-update.png" alt-text="Screenshot of the deploy model UI of Azure OpenAI Studio." lightbox="../media/models/auto-update.png":::
 
 You can learn more about Azure OpenAI model versions and how they work in the [Azure OpenAI model versions](../concepts/model-versions.md) article.
 
 ### Auto update to default
 
-When **Auto-update to default** is selected your model deployment will be automatically updated within two weeks of a change in the default version.  For a preview version, it will update automatically when a new preview version is available starting two weeks after the new preview version is released.
+When you set your deployment to **Auto-update to default**, your model deployment is automatically updated within two weeks of a change in the default version.  For a preview version, it updates automatically when a new preview version is available starting two weeks after the new preview version is released.
 
 If you're still in the early testing phases for inference models, we recommend deploying models with **auto-update to default** set whenever it's available.
 
 ### Specific model version
 
-As your use of Azure OpenAI evolves, and you start to build and integrate with applications you might want to manually control model updates so that you can first test and validate that model performance is remaining consistent for your use case prior to upgrade.
+As your use of Azure OpenAI evolves, and you start to build and integrate with applications you might want to manually control model updates. You can first test and validate that your application behavior is consistent for your use case before upgrading.
 
-When you select a specific model version for a deployment this version will remain selected until you either choose to manually update yourself, or once you reach the retirement date for the model. When the retirement date is reached the model will automatically upgrade to the default version at the time of retirement.
+When you select a specific model version for a deployment, this version remains selected until you either choose to manually update yourself, or once you reach the retirement date for the model. When the retirement date is reached the model will automatically upgrade to the default version at the time of retirement.
 
-## Viewing deprecation dates
+## Viewing retirement dates
 
 For currently deployed models, from Azure OpenAI Studio select **Deployments**:
 
 :::image type="content" source="../media/models/deployments.png" alt-text="Screenshot of the deployment UI of Azure OpenAI Studio." lightbox="../media/models/deployments.png":::
 
-To view deprecation/expiration dates for all available models in a given region from Azure OpenAI Studio select **Models** > **Column options** > Select **Deprecation fine tune** and **Deprecation inference**:
+To view retirement dates for all available models in a given region from Azure OpenAI Studio, select **Models** > **Column options** > Select **Deprecation fine tune** and **Deprecation inference**:
 
 :::image type="content" source="../media/models/column-options.png" alt-text="Screenshot of the models UI of Azure OpenAI Studio." lightbox="../media/models/column-options.png":::
 
@@ -54,28 +54,28 @@ You can check what model upgrade options are set for previously deployed models
 
 :::image type="content" source="../media/how-to/working-with-models/deployments.png" alt-text="Screenshot of the deployments pane with a deployment name highlighted." lightbox="../media/how-to/working-with-models/deployments.png":::
 
-This will open the **Properties** for the model deployment. You can view what upgrade options are set for your deployment under **Version update policy**:
+Selecting a deployment name opens the **Properties** for the model deployment. You can view what upgrade options are set for your deployment under **Version update policy**:
 
 :::image type="content" source="../media/how-to/working-with-models/update-policy.png" alt-text="Screenshot of the model deployments property UI." lightbox="../media/how-to/working-with-models/update-policy.png":::
 
 The corresponding property can also be accessed via [REST](../how-to/working-with-models.md#model-deployment-upgrade-configuration), [Azure PowerShell](/powershell/module/az.cognitiveservices/get-azcognitiveservicesaccountdeployment), and [Azure CLI](/cli/azure/cognitiveservices/account/deployment#az-cognitiveservices-account-deployment-show).
 
 |Option| Read | Update |
 |---|---|---|
-| [REST](../how-to/working-with-models.md#model-deployment-upgrade-configuration) | Yes. If `versionUpgradeOption` is not returned it means it is `null` |Yes |
+| [REST](../how-to/working-with-models.md#model-deployment-upgrade-configuration) | Yes. If `versionUpgradeOption` is not returned, it means it is `null` |Yes |
 | [Azure PowerShell](/powershell/module/az.cognitiveservices/get-azcognitiveservicesaccountdeployment) | Yes.`VersionUpgradeOption` can be checked for `$null`| Yes |
 | [Azure CLI](/cli/azure/cognitiveservices/account/deployment#az-cognitiveservices-account-deployment-show) | Yes. It shows `null` if `versionUpgradeOption` is not set.| *No.* It is currently not possible to update the version upgrade option.|
 
 There are three distinct model deployment upgrade options:
 
 | Name | Description |
 |------|--------|
-| `OnceNewDefaultVersionAvailable` | Once a new version is designated as the default, the model deployment will automatically upgrade to the default version within two weeks of that designation change being made. |
-|`OnceCurrentVersionExpired` | Once the retirement date is reached the model deployment will automatically upgrade to the current default version. |
-|`NoAutoUpgrade` | The model deployment will never automatically upgrade. Once the retirement date is reached the model deployment will stop working. You will need to update your code referencing that deployment to point to a nonexpired model deployment. |
+| `OnceNewDefaultVersionAvailable` | Once a new version is designated as the default, the model deployment automatically upgrades to the default version within two weeks of that designation change being made. |
+|`OnceCurrentVersionExpired` | Once the retirement date is reached the model deployment automatically upgrades to the current default version. |
+|`NoAutoUpgrade` | The model deployment never automatically upgrades. Once the retirement date is reached the model deployment stops working. You need to update your code referencing that deployment to point to a nonexpired model deployment. |
 
 > [!NOTE]
-> `null` is equivalent to `AutoUpgradeWhenExpired`. If the **Version update policy** option is not present in the properties for a model that supports model upgrades this indicates the value is currently `null`. Once you explicitly modify this value the property will be visible in the studio properties page as well as via the REST API.
+> `null` is equivalent to `AutoUpgradeWhenExpired`. If the **Version update policy** option is not present in the properties for a model that supports model upgrades this indicates the value is currently `null`. Once you explicitly modify this value, the property is visible in the studio properties page as well as via the REST API.
 
 ### Examples
 
@@ -122,7 +122,7 @@ New-AzCognitiveServicesAccountDeployment -ResourceGroupName {ResourceGroupName}
 
 # [REST](#tab/rest)
 
-To query the current model deployment settings including the deployment upgrade configuration for a given resource use [`Deployments List`](/rest/api/cognitiveservices/accountmanagement/deployments/list?tabs=HTTP#code-try-0). If the value is null you won't see a `versionUpgradeOption` property.
+To query the current model deployment settings including the deployment upgrade configuration for a given resource use [`Deployments List`](/rest/api/cognitiveservices/accountmanagement/deployments/list?tabs=HTTP#code-try-0). If the value is null, you won't see a `versionUpgradeOption` property.
 
 ```http
 GET https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CognitiveServices/accounts/{accountName}/deployments?api-version=2023-05-01

articles/azure-monitor/essentials/prometheus-metrics-overview.md

@@ -5,7 +5,7 @@ author: EdB-MSFT
 ms.service: azure-monitor
 ms-author: edbaynash
 ms.topic: conceptual
-ms.date: 05/10/2023
+ms.date: 01/25/2024
 ---
 
 # Azure Monitor managed service for Prometheus
@@ -45,13 +45,14 @@ See [Azure Monitor service limits](../service-limits.md#prometheus-metrics) for
 ## Limitations/Known issues - Azure Monitor managed Service for Prometheus
 
 - Scraping and storing metrics at frequencies less than 1 second isn't supported.
-- Metrics with the same label names but different cases are rejected during ingestion (ex;- `diskSize(cluster="eastus", node="node1", filesystem="usr_mnt", FileSystem="usr_opt")` is invalid due to `filesystem` and `FileSystem` labels, and are rejected).
 - Microsoft Azure operated by 21Vianet cloud and Air gapped clouds aren't supported for Azure Monitor managed service for Prometheus.
 - To monitor Windows nodes & pods in your cluster(s), follow steps outlined [here](../containers/kubernetes-monitoring-enable.md#enable-windows-metrics-collection-preview).
 - Azure Managed Grafana isn't currently available in the Azure US Government cloud.
 - Usage metrics (metrics under `Metrics` menu for the Azure Monitor workspace) - Ingestion quota limits and current usage for any Azure monitor Workspace aren't available yet in US Government cloud.
 - During node updates, you might experience gaps lasting 1 to 2 minutes in some metric collections from our cluster level collector. This gap is due to a regular action from Azure Kubernetes Service to update the nodes in your cluster. This behavior is expected and occurs due to the node it runs on being updated. None of our recommended alert rules are affected by this behavior. 
 
+[!INCLUDE [case sensitivity](../includes/prometheus-case-sensitivity.md)] 
+
 ## Prometheus references
 Following are links to Prometheus documentation.
 
@@ -88,4 +89,4 @@ If you use the Azure portal to enable Prometheus metrics collection and install
 
 - [Enable Azure Monitor managed service for Prometheus on your Kubernetes clusters](../containers/kubernetes-monitoring-enable.md).
 - [Configure Prometheus alerting and recording rules groups](prometheus-rule-groups.md).
-- [Customize scraping of Prometheus metrics](prometheus-metrics-scrape-configuration.md).
+- [Customize scraping of Prometheus metrics](prometheus-metrics-scrape-configuration.md).

articles/azure-monitor/includes/prometheus-case-sensitivity.md

@@ -1,13 +1,16 @@
 ---
 ms.service: azure-monitor
 ms.topic: include
-ms.date: 04/01/2023
+ms.date: 01/25/2024
 ms.author: edbaynash
 author: EdB-MSFT
 ---
 
 ## Case sensitivity
-Azure managed Prometheus is a case insensitive system. If one time series differs from another only by a difference in the case of a string (metric name, label name, label value, etc), it's treated as the same time series. This behavior is different from native open source Prometheus, which is a case sensitive system.
+Azure managed Prometheus is a case insensitive system. It treats strings, such as metric names, label names, or label values, as the same time series if they differ from another time series only by the case of the string.
+
+> [!NOTE]
+> This behavior is different from native open source Prometheus, which is a case sensitive system.
 
 In Azure managed Prometheus the following time series are considered the same: 
 
@@ -16,10 +19,10 @@ In Azure managed Prometheus the following time series are considered the same:
 
 The above examples are a single time series in a time series database.
 -	Any samples ingested against them are stored as if they're scraped/ingested against a single time series.
--	If the examples above are ingested with the same timestamp, one of them is randomly dropped.
+-	If the preceding examples are ingested with the same timestamp, one of them is randomly dropped.
 -	The casing that's stored in the time series database and returned by a query is unpredictable. Different casing may be returned at different times for the same time series.
 -	Any metric name or label name/value matcher present in the query is retrieved from time series database by making a case-insensitive comparison. If there's a case sensitive matcher in a query, it's automatically treated as a case-insensitive matcher when making string comparisons.
 
-It is best practice to ensure that a time series is produced or scraped using a single consistent case.
+It's best practice to ensure that a time series is produced or scraped using a single consistent case.
 
-In OSS Prometheus, the above time series are treated as two different time series. Any samples scraped/ingested against them are stored separately.
+In Open Source Prometheus, the above time series are treated as two different time series. Any samples scraped/ingested against them are stored separately.

articles/azure-monitor/includes/prometheus-faq-i-am-missing-metrics-with-same-name-different-casing.md

@@ -8,4 +8,4 @@ author: EdB-MSFT
 
 ### Why am I missing metrics that have two labels with the same name but different casing?
 
-Metrics that have two label names that are the same except for their casing will be treated as having duplicate label names. These time series will be dropped upon ingestion since the two labels are seen as the same. For example, the time series `my_metric{ExampleLabel="label_value_0", examplelabel="label_value_1}` will be dropped due to duplicate labels since `ExampleLabel` and `examplelabel` will be seen as the same label name.
+Azure managed Prometheus is a case insensitive system. It treats strings, such as metric names, label names, or label values, as the same time series if they differ from another time series only by the case of the string. For more information, see [Prometheus metrics overview](/azure/azure-monitor/essentials/prometheus-metrics-overview#case-sensitivity).