Update how-to-monitor-datasets.md

Author: SturgeonMi

articles/machine-learning/v1/how-to-monitor-datasets.md

@@ -52,7 +52,7 @@ To create and work with dataset monitors, you need:
 * The [Azure Machine Learning SDK for Python installed](/python/api/overview/azure/ml/install), which includes the azureml-datasets package.
 * Structured (tabular) data with a timestamp specified in the file path, file name, or column in the data.
 
-### Migrate to Model Monitor
+## Migrate to Model Monitor
 When you migrate to Model Monitor, please check the prerequisites as following:
 
 
@@ -108,7 +108,7 @@ This top down approach makes it easy to monitor data instead of traditional rule
 
 In Azure Machine Learning, you use dataset monitors to detect and alert for data drift.
 
-### Dataset monitors
+## Dataset monitors
 
 With a dataset monitor you can:
 
@@ -333,7 +333,7 @@ When you migrate to Model Monitor, if you didn't deploy your model to production
 
 Following sections contain more details on how to migrate to Model Monitor.
 
-### If you have deployed your model to production in an Azure Machine Learning online endpoint and enabled data collection
+## If you have deployed your model to production in an Azure Machine Learning online endpoint and enabled data collection
 
 If you have deployed your model to production in an Azure Machine Learning online endpoint and enabled [data collection](../how-to-collect-production-data.md) at deployment time.
 
@@ -444,7 +444,7 @@ The following YAML contains the definition for the out-of-box model monitoring.
 :::code language="yaml" source="~/azureml-examples-main/cli/monitoring/out-of-box-monitoring.yaml":::
 ---
 
-### If you didn't deploy your model to production in an Azure Machine Learning online endpoint or you don't want to use data collection
+## If you didn't deploy your model to production in an Azure Machine Learning online endpoint or you don't want to use data collection
 When you migrate to Model Monitor, if you didn't deploy your model to production in an Azure Machine Learning online endpoint, or you don't want to use [data collection](../how-to-collect-production-data.md), you can also [set up model monitoring with custom signals and metrics](../how-to-monitor-model-performance.md#set-up-model-monitoring-with-custom-signals-and-metrics).
 
 You can also set up model monitoring for models deployed to Azure Machine Learning batch endpoints or deployed outside of Azure Machine Learning. If you don't have a deployment, but you have production data, you can use the data to perform continuous model monitoring. To monitor these models, you must be able to: