fix

Author: v-thepet

articles/machine-learning/concept-endpoints-online.md

@@ -9,7 +9,7 @@ ms.topic: concept-article
 author: msakande
 ms.author: mopeakande
 ms.reviewer: sehan
-ms.custom: devplatv2
+ms.custom: devplatv2, FY25Q1-Linter
 ms.date: 09/23/2024
 
 #Customer intent: As an ML pro, I want to understand what an online endpoint is and why I need it.
@@ -21,6 +21,8 @@ ms.date: 09/23/2024
 
 This article describes online endpoints for real-time inferencing in Azure Machine Learning. Inferencing is the process of applying new input data to a machine learning model to generate outputs. Azure Machine Learning allows you to perform real-time inferencing on data by using models that are deployed to *online endpoints*. While these outputs are typically called *predictions*, you can use inferencing to generate outputs for other machine learning tasks, such as classification and clustering.
 
+## Online endpoints
+
 Online endpoints deploy models to a web server that can return predictions under the HTTP protocol. Online endpoints can operationalize models for real-time inference in synchronous, low-latency requests, and are best used when:
 
 - You have low-latency requirements.

articles/machine-learning/how-to-troubleshoot-online-endpoints.md

@@ -159,6 +159,7 @@ ml_client.online_deployments.get_logs(
 
 ### [Studio](#tab/studio)
 
+<a name="see-log-output-in-azure-machine-learning-studio"</a>
 To view log output from a container in Azure Machine Learning studio:
 
 1. Select **Endpoints** in the left navigation bar.