Update how-to-monitor-datasets.md

Author: SturgeonMi

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

@@ -53,44 +53,9 @@ To create and work with dataset monitors, you need:
 * Structured (tabular) data with a timestamp specified in the file path, file name, or column in the data.
 
 ## Prerequisites (Migrate to Model Monitor)
-When you migrate to Model Monitor, please check the prerequisites as following:
+When you migrate to Model Monitor, please check the prerequisites as mentioned in this article [Prerequisites of Azure Machine Learning model monitoring](../how-to-monitor-model-performance.md#prerequisites).
 
 
-# [Python SDK](#tab/python)
-
-[!INCLUDE [basic prereqs sdk](../includes/machine-learning-sdk-v2-prereqs.md)]
-
-# [Studio](#tab/azure-studio)
-
-Before following the steps in this article, make sure you have the following prerequisites:
-
-* An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/).
-
-* An Azure Machine Learning workspace and a compute instance. If you don't have these resources, use the steps in the [Quickstart: Create workspace resources](../quickstart-create-resources.md) article to create them.
-
-# [Azure CLI](#tab/azure-cli)
-
-[!INCLUDE [basic prereqs cli](../includes/machine-learning-cli-prereqs.md)]
----
-
-* Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure Machine Learning. To perform the steps in this article, your user account must be assigned the __owner__ or __contributor__ role for the Azure Machine Learning workspace, or a custom role allowing `Microsoft.MachineLearningServices/workspaces/onlineEndpoints/*`. For more information, see [Manage access to an Azure Machine Learning workspace](../how-to-assign-roles.md).
-
-*  For monitoring a model that is deployed to an Azure Machine Learning online endpoint (managed online endpoint or Kubernetes online endpoint), be sure to:
-
-    * Have a model already deployed to an Azure Machine Learning online endpoint. Both managed online endpoint and Kubernetes online endpoint are supported. If you don't have a model deployed to an Azure Machine Learning online endpoint, see [Deploy and score a machine learning model by using an online endpoint](../how-to-deploy-online-endpoints.md).
-
-    * Enable data collection for your model deployment. You can enable data collection during the deployment step for Azure Machine Learning online endpoints. For more information, see [Collect production data from models deployed to a real-time endpoint](../how-to-collect-production-data.md).
-
-*  For monitoring a model that is deployed to an Azure Machine Learning batch endpoint or deployed outside of Azure Machine Learning, be sure to:
-
-    * Have a means to collect production data and register it as an Azure Machine Learning data asset.
-    * Update the registered data asset continuously for model monitoring.
-    * (Recommended) Register the model in an Azure Machine Learning workspace, for lineage tracking.
-
-> [!IMPORTANT]
->
-> Model monitoring jobs are scheduled to run on serverless Spark compute pools with support for the following VM instance types: `Standard_E4s_v3`, `Standard_E8s_v3`, `Standard_E16s_v3`, `Standard_E32s_v3`, and `Standard_E64s_v3`. You can select the VM instance type with the `create_monitor.compute.instance_type` property in your YAML configuration or from the dropdown in the Azure Machine Learning studio.
-
 ## What is data drift?
 
 Model accuracy degrades over time, largely because of data drift. For machine learning models, data drift is the change in model input data that leads to model performance degradation. Monitoring data drift helps detect these model performance issues.
@@ -233,7 +198,7 @@ The **backfill** function runs a backfill job, for a specified start and end dat
 > Azure Machine Learning model monitoring doesn't support manual **backfill** function, if you want to redo the model monitor for a specif time range, you can create another model monitor for that specific time range.
 
 # [Python SDK](#tab/python)
-
+<a name="sdk-monitor"></a>
 
 [!INCLUDE [sdk v1](../includes/machine-learning-sdk-v1.md)]
 
@@ -290,7 +255,7 @@ monitor = monitor.enable_schedule()
 
 
 # [Studio](#tab/azure-studio)
-
+<a name="studio-monitor"></a>
 
 1. Navigate to the [studio's homepage](https://ml.azure.com).
 1. Select the **Data** tab.