Merge pull request #1571 from lgayhardt/onlineeval2

AI Studio: Online Eval

Author: v-regandowner

articles/ai-studio/how-to/develop/trace-production-sdk.md

@@ -21,6 +21,9 @@ After deploying a Generative AI APP in production, you might want to enhance you
 
 In this article, you learn to enable tracing, collect aggregated metrics, and collect user feedback during inference time of your flow deployment.
 
+> [!NOTE]
+> For an improved way to perform continuous monitoring of deployed applications (other than prompt flow), consider using [Azure AI online evaluation](../online-evaluation.md).
+
 ## Prerequisites
 
 - The Azure CLI and the Azure Machine Learning extension to the Azure CLI. 

articles/ai-studio/how-to/monitor-quality-safety.md

@@ -22,7 +22,7 @@ author: msakande
 Monitoring applications that are deployed to production is an essential part of the generative AI application lifecycle. Changes in data and consumer behavior can influence your application over time, resulting in outdated systems that negatively affect business outcomes and expose organizations to compliance, economic, and reputation risks. 
 
 > [!NOTE]
-> For an improved way to perform continuous monitoring of deployed applications (other than prompt flow), consider using Azure AI online evaluation.
+> For an improved way to perform continuous monitoring of deployed applications (other than prompt flow), consider using [Azure AI online evaluation](./online-evaluation.md).
 
 Azure AI monitoring for generative AI applications enables you to monitor your applications in production for token usage, generation quality, and operational metrics.
 

articles/ai-studio/how-to/online-evaluation.md

@@ -301,6 +301,8 @@ items:
          href: how-to/develop/trace-local-sdk.md
        - name: Visualize your traces
          href: how-to/develop/visualize-traces.md
+       - name: Continuously monitor your applications
+         href: how-to/online-evaluation.md
   - name: Evaluate generative AI apps
     items:
     - name: Evaluations concepts