update

Author: mrbullwinkle

articles/ai-services/openai/how-to/batch.md

@@ -80,7 +80,7 @@ In the Studio UI the deployment type will appear as `Global-Batch`.
 :::image type="content" source="../media/how-to/global-batch/global-batch.png" alt-text="Screenshot that shows the model deployment dialog in Azure OpenAI Studio with Global-Batch deployment type highlighted." lightbox="../media/how-to/global-batch/global-batch.png":::
 
 > [!TIP]
-> Each line of your input file for batch processing has a model attribute that requires a global batch deployment name. For a given input file, all names must be the same deployment name. This is different from OpenAI where the concept of model deployments does not exist.
+> Each line of your input file for batch processing has a `model` attribute that requires a global batch **deployment name**. For a given input file, all names must be the same deployment name. This is different from OpenAI where the concept of model deployments does not exist.
 
 ::: zone pivot="programming-language-ai-studio"
 

articles/ai-services/openai/how-to/deployment-types.md

@@ -30,7 +30,7 @@ Azure OpenAI offers three types of deployments. These provide a varied level of
 
 | **Offering** | **Global-Batch** | **Global-Standard** | **Standard** | **Provisioned**  |
 |---|:---|:---|:---|:---|
-| **Best suited for**      | Offline scoring <br><br> Workloads that are not latency sensitive and can be completed in hours.<br><br> For use cases that do not have data processing residency requirements.| Recommended starting place for customers. <br><br>Global-Standard will have the higher default quota and larger number of models available than Standard. <br><br> For production applications that do not have data processing residency requirements. | For customers with data processing residency requirements. | For customers with data residency requirements. Optimized for low to medium volume. | Real-time scoring for large consistent volume. Includes the highest commitments and limits.|
+| **Best suited for**      | Offline scoring <br><br> Workloads that are not latency sensitive and can be completed in hours.<br><br> For use cases that do not have data processing residency requirements.| Recommended starting place for customers. <br><br>Global-Standard will have the higher default quota and larger number of models available than Standard. <br><br> For production applications that do not have data processing residency requirements. | For customers with data residency requirements. Optimized for low to medium volume. | Real-time scoring for large consistent volume. Includes the highest commitments and limits.|
 | **How it works**         | Offline processing via files |Traffic may be routed anywhere in the world | | |
 | **Getting started**      | [Global-Batch](./batch.md) | [Model deployment](./create-resource.md) | [Model deployment](./create-resource.md) | [Provisioned onboarding](./provisioned-throughput-onboarding.md) |
 | **Cost**                 | [Least expensive option](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) <br> 50% less cost compared to Global Standard prices. Access to all new models with larger quota allocations.  | [Global deployment pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) |  [Regional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) | May experience cost savings for consistent usage |