acrolinx

Author: lgayhardt

articles/ai-foundry/how-to/develop/cloud-evaluation.md

@@ -28,15 +28,15 @@ In this article, you learn how to run evaluations in the cloud (preview) in pre-
 - Azure OpenAI Deployment with GPT model supporting `chat completion`, for example `gpt-4`.
 - Make sure you're first logged into your Azure subscription by running `az login`.
 
-If this is your first time running evaluations and logging it to your Azure AI Foundry project, you may need to do a few additional set up steps.
+If this is your first time running evaluations and logging it to your Azure AI Foundry project, you might need to do a few additional setup steps.
 
 1. [Create and connect your storage account](https://github.com/azure-ai-foundry/foundry-samples/blob/main/samples/microsoft/infrastructure-setup/01-connections/connection-storage-account.bicep) to your Azure AI Foundry project at the resource level. This bicep template provisions and connects a storage account to your Foundry project with key authentication.
 2. Make sure the connected storage account has access to all projects.
-3. If you connected your storage account with Entra ID, make sure to give MSI (Microsoft Identity) permissions for Storage Blob Data Owner to both your account and Foundry project resource in Azure Portal.
+3. If you connected your storage account with Microsoft Entra ID, make sure to give MSI (Microsoft Identity) permissions for Storage Blob Data Owner to both your account and Foundry project resource in Azure portal.
 
 ### Getting started
 
-First, install Azure AI Foundry SDK's project client which will run the evaluations in the cloud
+First, install Azure AI Foundry SDK's project client which runs the evaluations in the cloud
 
 ```python
 uv install azure-ai-projects azure-identity
@@ -60,7 +60,7 @@ dataset_name    = os.environ.get("DATASET_NAME",    "dataset-test")
 dataset_version = os.environ.get("DATASET_VERSION", "1.0")
 ```
 
-Now you can define a client which will be used to run your evaluations in the cloud:
+Now you can define a client which is used to run your evaluations in the cloud:
 
 ```python
 import os
@@ -151,7 +151,7 @@ print("Status:", evaluation_response.status)
 ## Specifying custom evaluators
 
 > [!NOTE]
-> Azure AI Foundry Projects are not supported for this feature, use an Azure AI Hub Project instead.
+> Azure AI Foundry Projects aren't supported for this feature. Use an Azure AI Hub Project instead.
 
 ### Code-based custom evaluators