diff --git a/sn_collections/_workshops/2024-ai-user-forum/20-a-ml-intro.md b/sn_collections/_workshops/2024-ai-user-forum/20-a-ml-intro.md
index d53b4ce74..3c0c30dc3 100644
--- a/sn_collections/_workshops/2024-ai-user-forum/20-a-ml-intro.md
+++ b/sn_collections/_workshops/2024-ai-user-forum/20-a-ml-intro.md
@@ -14,3 +14,62 @@ prerequisites:
 Machine learning underlies the vast majority of modern AI methods, including the ever-expanding applications of deep learning and generative AI. This workshop will give participants a hands-on introduction to the basic concepts and techniques needed to understand machine learning and to apply machine learning methods to scientific research. <!--excerpt--> 
 
 Participants will learn how to train, evaluate, and use a variety of machine learning models for data analysis tasks. This session will also help participants critically evaluate the use and application of machine learning in science.
+
+
+## Tutorial setup instruction
+
+Steps to prepare for the tutorial:
+
+1. **Login to Atlas Open OnDemand** at [https://atlas-ood.hpc.msstate.edu/](https://atlas-ood.hpc.msstate.edu/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide]({{site.baseurl}}/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on "Clusters" -> "Atlas Shell Access" on the top menu. This will open a new tab with a command-line session on Atlas's login node.
+
+1. **Request resources on a compute node** by running the following command: 
+
+    {:.copy-code}
+    ```bash
+srun --reservation=forum -A scinet_workshop1 -t 00:30:00 -n 1 --mem 8G --pty bash 
+```
+
+1. **Create and/or update your workshop working directory** and copy the tutorial materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the `$USER` variable. 
+
+    {:.copy-code}
+    ```bash
+mkdir -p /90daydata/shared/$USER/intro_ml
+cd /90daydata/shared/$USER/intro_ml
+cp -r /project/ai_forum/intro_ml/intro_ml.ipynb .
+```
+
+1. **Setup the kernel for JupyterLab.** You will create a kernel called *intro_ml_env* to access from JupyterLab Server. Run the following commands to activate the workshop's virtual environment and create a new kernelspec from it:
+
+    {:.copy-code}
+    ```bash
+source /project/ai_forum/intro_ml/intro_ml_env/bin/activate
+ipython kernel install --name "intro_ml_env" --user
+```
+
+1. **Stop the interactive job** on the compute node by running the command:
+
+    {:.copy-code}
+    ```bash
+exit
+```
+
+1. **Launch a JupyterLab Server session.** Under the *Interactive Apps* menu, select *JupyterLab Server*. Specify the following input values on the page:
+
+    * Account: scinet_workshop1
+    * Partition: atlas
+    * QOS: normal 14-00:00:00
+    * Number of hours: 4
+    * Number of nodes: 1
+    * Number of tasks: 6
+    * Additional Slurm Parameters: \-\-reservation=forum \-\-mem=32G
+    * Working Directory: /90daydata/shared/${USER}/intro_ml
+  
+    Click *Launch*. The screen will update to the *Interactive Sessions* page. When your Jupyter session is ready, the top card will update from *Queued* to *Running* and a *Connect to JupyterLab Server* button will appear. Click *Connect to JupyterLab Server*.
+
+1. **Open the `intro_ml.ipynb` notebook.**
+  
+1. **Select the `intro_ml_env` kernel** for the notebook.
+
+