articles-about-evaluating-apps-with-Azure-AI-Evaluation-SDK

Author: LHL407

articles/ai-foundry/how-to/develop/agent-evaluate-sdk.md

@@ -1,7 +1,7 @@
 ---
-title: Cloud evaluation with Azure AI Foundry SDK
+title: Cloud Evaluation with the Azure AI Foundry SDK
 titleSuffix: Azure AI Foundry
-description: This article provides instructions on how to evaluate a Generative AI application on the cloud.
+description: This article provides instructions on how to evaluate a generative AI application in the cloud.
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.custom:
@@ -13,54 +13,53 @@ ms.reviewer: changliu2
 ms.author: lagayhar
 author: lgayhardt
 ---
-# Run evaluations in the cloud using Azure AI Foundry SDK (preview)
+# Run evaluations in the cloud by using the Azure AI Foundry SDK (preview)
 
 [!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
-While Azure AI Evaluation SDK supports running evaluations locally on your own machine, you might want to delegate the job remotely to the cloud. For example, after you ran local evaluations on small test data to help assess your generative AI application prototypes, now you move into pre-deployment testing and need run evaluations on a large dataset. Cloud evaluation frees you from managing your local compute infrastructure, and enables you to integrate evaluations as tests into your CI/CD pipelines. After deployment, you might want to [continuously evaluate](../online-evaluation.md) your applications for post-deployment monitoring.
+The Azure AI Evaluation SDK supports running evaluations locally on your own machine and also in the cloud. For example, after you ran local evaluations on small test data to help assess your generative AI application prototypes, you can move into pre-deployment testing and run evaluations on a large dataset. Evaluating your applications in the cloud frees you from managing your local compute infrastructure, and enables you to integrate evaluations as tests into your continuous integration and continuous delivery (CI/CD) pipelines. After deployment, you can choose to [continuously evaluate](../online-evaluation.md) your applications for post-deployment monitoring.
 
-In this article, you learn how to run evaluations in the cloud (preview) in pre-deployment testing on a test dataset. Using the Azure AI Projects SDK, you'll have evaluation results automatically logged into your Azure AI project for better observability. This feature supports all Microsoft curated [built-in evaluators](../../concepts/observability.md#what-are-evaluators) and your own [custom evaluators](../../concepts/evaluation-evaluators/custom-evaluators.md) which can be located in the [Evaluator library](../evaluate-generative-ai-app.md#view-and-manage-the-evaluators-in-the-evaluator-library) and have the same project-scope RBAC.
+In this article, you learn how to run evaluations in the cloud (preview) in pre-deployment testing on a test dataset. When you use the Azure AI Projects SDK, evaluation results are automatically logged into your Azure AI project for better observability. This feature supports all Microsoft curated [built-in evaluators](../../concepts/observability.md#what-are-evaluators) and your own [custom evaluators](../../concepts/evaluation-evaluators/custom-evaluators.md). Your evaluators can be located in the [Evaluator library](../evaluate-generative-ai-app.md#view-and-manage-the-evaluators-in-the-evaluator-library) and have the same project-scope role-based access control (RBAC).
 
-## Prerequisite set up steps for Azure AI Foundry Projects
+## Prerequisites
 
-- Azure AI Foundry project in the same supported [regions](../../concepts/evaluation-evaluators/risk-safety-evaluators.md#azure-ai-foundry-project-configuration-and-region-support) as risk and safety evaluators (preview). If you don't have an existing project, follow the guide [How to create Azure AI Foundry project](../create-projects.md?tabs=ai-studio) to create one.
-
-- Azure OpenAI Deployment with GPT model supporting `chat completion`, for example `gpt-4`.
+- Azure AI Foundry project in the same supported [regions](../../concepts/evaluation-evaluators/risk-safety-evaluators.md#azure-ai-foundry-project-configuration-and-region-support) as risk and safety evaluators (preview). If you don't have an existing project, create one by following the guide [How to create Azure AI Foundry project](../create-projects.md?tabs=ai-studio).
+- 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 might need to do a few additional setup 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 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.
+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. The [bicep template](https://github.com/azure-ai-foundry/foundry-samples/blob/main/samples/microsoft/infrastructure-setup/01-connections/connection-storage-account.bicep) provisions and connects a storage account to your Foundry project by using key authentication.
 2. Make sure the connected storage account has access to all projects.
-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.
+3. If you connected your storage account with Microsoft Entra ID, make sure to give MSI (Microsoft Identity) **Storage Blob Data Owner** permissions to both your account and the Foundry project resource in the Azure portal.
 
-### Getting started
+### Get started
 
-First, install Azure AI Foundry SDK's project client which runs the evaluations in the cloud
+1. Install the Azure AI Foundry SDK project client that runs the evaluations in the cloud.
 
 ```python
 uv install azure-ai-projects azure-identity
 ```
 
 > [!NOTE]
-> For more detailed information, see the [REST API Reference Documentation](/rest/api/aifoundry/aiprojects/evaluations).
-Then, set your environment variables for your Azure AI Foundry resources
+> For more detailed information, see [REST API Reference Documentation](/rest/api/aifoundry/aiprojects/evaluations).
+
+1. Set your environment variables for your Azure AI Foundry resources
 
 ```python
 import os
 
 # Required environment variables
 endpoint = os.environ["PROJECT_ENDPOINT"] # https://<account>.services.ai.azure.com/api/projects/<project>
 model_endpoint = os.environ["MODEL_ENDPOINT"] # https://<account>.services.ai.azure.com
-model_api_key = os.environ["MODEL_API_KEY"]
-model_deployment_name = os.environ["MODEL_DEPLOYMENT_NAME"] # e.g. gpt-4o-mini
+model_api_key = os.environ["MODEL_API_KEY"] 
 
 # Optional – reuse an existing dataset
 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 is used to run your evaluations in the cloud:
+Now, you can define a client that runs your evaluations in the cloud:
 
 ```python
 import os
@@ -74,22 +73,22 @@ project_client = AIProjectClient(
 )
 ```
 
-## Uploading evaluation data
+## Upload evaluation data
 
 ```python
-# Upload a local jsonl file (skip if you already have a Dataset registered)
+# Upload a local JSONL file (skip if you already have a Dataset registered)
 data_id = project_client.datasets.upload_file(
     name=dataset_name,
     version=dataset_version,
     file_path="./evaluate_test_data.jsonl",
 ).id
 ```
 
-To learn more about input data formats for evaluating GenAI applications, see [single-turn data](./evaluate-sdk.md#single-turn-support-for-text), [conversation data](./evaluate-sdk.md#conversation-support-for-text), and [conversation data for images and multi-modalities](./evaluate-sdk.md#conversation-support-for-images-and-multi-modal-text-and-image).
+To learn more about input data formats for evaluating generative AI applications, see [Single-turn data](./evaluate-sdk.md#single-turn-support-for-text), [Conversation data](./evaluate-sdk.md#conversation-support-for-text), and [Conversation data for images and multi-modalities](./evaluate-sdk.md#conversation-support-for-images-and-multi-modal-text-and-image).
 
-To learn more about input data formats for evaluating agents, see [evaluating Azure AI agents](./agent-evaluate-sdk.md#evaluate-azure-ai-agents) and [evaluating other agents](./agent-evaluate-sdk.md#evaluating-other-agents).
+To learn more about input data formats for evaluating agents, see [Evaluating Azure AI agents](./agent-evaluate-sdk.md#evaluate-azure-ai-agents) and [Evaluating other agents](./agent-evaluate-sdk.md#evaluating-other-agents).
 
-## Specifying evaluators
+## Specify evaluators
 
 ```python
 from azure.ai.projects.models import (
@@ -117,7 +116,7 @@ evaluators = {
 }
 ```
 
-## Submit evaluation in the cloud
+## Submit an evaluation in the cloud
 
 Finally, submit the remote evaluation run:
 
@@ -148,10 +147,10 @@ print("Created evaluation:", evaluation_response.name)
 print("Status:", evaluation_response.status)
 ```
 
-## Specifying custom evaluators
+## Specify custom evaluators
 
 > [!NOTE]
-> Azure AI Foundry Projects aren't 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 Foundry hub project instead.
 
 ### Code-based custom evaluators
 
@@ -194,7 +193,7 @@ versioned_evaluator = ml_client.evaluators.get(evaluator_name, version=1)
 print("Versioned evaluator id:", registered_evaluator.id)
 ```
 
-After registering your custom evaluator to your Azure AI project, you can view it in your [Evaluator library](../evaluate-generative-ai-app.md#view-and-manage-the-evaluators-in-the-evaluator-library) under **Evaluation** tab in your Azure AI project.
+After you register your custom evaluator to your Azure AI project, you can view it in your [Evaluator library](../evaluate-generative-ai-app.md#view-and-manage-the-evaluators-in-the-evaluator-library) under the **Evaluation** tab in your Azure AI project.
 
 ### Prompt-based custom evaluators
 
@@ -242,13 +241,13 @@ versioned_evaluator = ml_client.evaluators.get(evaluator_name, version=1)
 print("Versioned evaluator id:", registered_evaluator.id)
 ```
 
-After logging your custom evaluator to your Azure AI project, you can view it in your [Evaluator library](../evaluate-generative-ai-app.md#view-and-manage-the-evaluators-in-the-evaluator-library) under **Evaluation** tab of your Azure AI project.
+After you log your custom evaluator to your Azure AI project, you can view it in your [Evaluator library](../evaluate-generative-ai-app.md#view-and-manage-the-evaluators-in-the-evaluator-library) under the **Evaluation** tab of your Azure AI project.
 
 ## Related content
 
-- [Evaluate your Generative AI applications locally](./evaluate-sdk.md)
-- [Evaluate your Generative AI applications online](https://aka.ms/GenAIMonitoringDoc)
+- [Evaluate your generative AI applications locally](./evaluate-sdk.md)
+- [Evaluate your generative AI applications online](https://aka.ms/GenAIMonitoringDoc)
 - [Learn more about simulating test datasets for evaluation](./simulator-interaction-data.md)
-- [View your evaluation results in Azure AI project](../../how-to/evaluate-results.md)
-- [Get started building a chat app using the Azure AI Foundry SDK](../../quickstarts/get-started-code.md)
+- [View your evaluation results in an Azure AI project](../../how-to/evaluate-results.md)
+- [Get started building a chat app by using the Azure AI Foundry SDK](../../quickstarts/get-started-code.md)
 - [Get started with evaluation samples](https://aka.ms/aistudio/eval-samples)