Microsoft Sentinel
Cloud and workloadsPrivate endpoint
In brief
The documentation now provides clearer wording for private-endpoint Azure Machine Learning workspace creation and explains notebook code execution, kernel output, variable persistence, and expression results. Sample output formatting and page metadata were also updated.
What Defender admins need to know
Administrators get clearer guidance for configuring the workspace and confirming that notebook cells and the kernel are working.
Summaries are generated from the documentation change itself.
Documentation change
View on Microsoft LearnThe comparison below shows only the changed extract. Use the full-page view for complete context.
author: EdB-MSFT
ms.author: edbaynash
ms.topic: how-to
ms.date: 06/15/07/01/2026
appliesto:
- Microsoft Sentinel in the Microsoft Defender portal
- Microsoft Sentinel in the Azure portal
ms.collection: usx-security
ms.custom: sfi-image-nochange, msecd-doc-authoring-10141016
ai-usage: ai-assisted
#Customer intent: As a security analyst, I want to deploy and launch a Jupyter notebook to hunt for security threats.
Private endpoint
The steps in this private endpoint workspace creationfollowing procedure reference specific articles in thefor creating a private-endpoint Azure Machine Learning documentationworkspace references specific Azure Machine Learning articles when relevant. For more information, see How to create a secure Azure Machine Learning workspace.
- Create a virtual machine (VM) jump box within a virtual network. Since the virtual network restricts access from the public internet, the jump box is used as a way to connect to resources behind the virtual network.
- Markdown cells have text, including HTML, and static images.
- Code cells contain code. After you select a code cell, run the code in the cell by selecting the Play icon to the left of the cell, or by pressing SHIFT+ENTER.
For example,The following sample code cell demonstrates basic Python operations and shows how code cells run in the notebook environment. Run the following code cell in your notebook:
# This is your first code cell. This cell contains basic Python code.
TheAfter you run the sample code produces this output:cell, the notebook displays the following output, confirming that the notebook kernel is working correctly:
Congratulations, you just ran this code cell
2 + 2 = 4
Variables set within a notebook code cell persist between cells, so you can chain cells together. For example, the following code cell uses the value of y from the previous cell:cell. This example also shows how Jupyter automatically displays the result of the last expression in a cell, even if you don't call print():
# Note that output from the last line of a cell is automatically
y + 2
The notebook displays the evaluated result of the expression y + 2 in the output is:area:
6
@@ -4,12 +4,12 @@ description: Launch and run notebooks with the Microsoft Sentinel hunting capabi author: EdB-MSFT ms.author: edbaynash ms.topic: how-to-ms.date: 06/15/2026+ms.date: 07/01/2026 appliesto: - Microsoft Sentinel in the Microsoft Defender portal - Microsoft Sentinel in the Azure portal ms.collection: usx-security-ms.custom: sfi-image-nochange, msecd-doc-authoring-1014+ms.custom: sfi-image-nochange, msecd-doc-authoring-1016 ai-usage: ai-assisted #Customer intent: As a security analyst, I want to deploy and launch a Jupyter notebook to hunt for security threats. @@ -79,7 +79,7 @@ Use the following steps to create an Azure Machine Learning workspace with a pub # [Private endpoint](#tab/private-endpoint) -The steps in this private endpoint workspace creation procedure reference specific articles in the Azure Machine Learning documentation when relevant. For more information, see [How to create a secure Azure Machine Learning workspace](/azure/machine-learning/tutorial-create-secure-workspace).+The following procedure for creating a private-endpoint Azure Machine Learning workspace references specific Azure Machine Learning articles when relevant. For more information, see [How to create a secure Azure Machine Learning workspace](/azure/machine-learning/tutorial-create-secure-workspace). 1. Create a virtual machine (VM) jump box within a virtual network. Since the virtual network restricts access from the public internet, the jump box is used as a way to connect to resources behind the virtual network. @@ -205,7 +205,7 @@ In a notebook: - **Markdown** cells have text, including HTML, and static images. - **Code** cells contain code. After you select a code cell, run the code in the cell by selecting the **Play** icon to the left of the cell, or by pressing **SHIFT+ENTER**. -For example, run the following code cell in your notebook:+The following sample code cell demonstrates basic Python operations and shows how code cells run in the notebook environment. Run the following code cell in your notebook: ```python # This is your first code cell. This cell contains basic Python code.@@ -222,15 +222,15 @@ print("2 + 2 =", y) ``` -The sample code produces this output:+After you run the sample cell, the notebook displays the following output, confirming that the notebook kernel is working correctly: -```python+```output Congratulations, you just ran this code cell 2 + 2 = 4 ``` -Variables set within a notebook code cell persist between cells, so you can chain cells together. For example, the following code cell uses the value of `y` from the previous cell:+Variables set within a notebook code cell persist between cells, so you can chain cells together. For example, the following code cell uses the value of `y` from the previous cell. This example also shows how Jupyter automatically displays the result of the last expression in a cell, even if you don't call `print()`: ```python # Note that output from the last line of a cell is automatically@@ -239,7 +239,7 @@ Variables set within a notebook code cell persist between cells, so you can chai y + 2 ``` -The output is:+The notebook displays the evaluated result of the expression `y + 2` in the output area: ```output 6