diff --git a/ai-ml/jupyterhub/examples/docker/elyra-updated-jupyterhub.Dockerfile b/ai-ml/jupyterhub/examples/docker/elyra-updated-jupyterhub.Dockerfile
new file mode 100644
index 000000000..abf465ac5
--- /dev/null
+++ b/ai-ml/jupyterhub/examples/docker/elyra-updated-jupyterhub.Dockerfile
@@ -0,0 +1,3 @@
+FROM elyra/elyra:3.15.0
+
+RUN pip install --upgrade jupyterhub==4.0.2
diff --git a/ai-ml/jupyterhub/helm/jupyterhub/jupyterhub-values-dummy.yaml b/ai-ml/jupyterhub/helm/jupyterhub/jupyterhub-values-dummy.yaml
index 12873189e..24320e22a 100755
--- a/ai-ml/jupyterhub/helm/jupyterhub/jupyterhub-values-dummy.yaml
+++ b/ai-ml/jupyterhub/helm/jupyterhub/jupyterhub-values-dummy.yaml
@@ -25,6 +25,17 @@ proxy:
 singleuser:
   startTimeout: 1200 # 20 mins to spin up a notebook server for GPU including the image pull
   profileList:
+    - display_name: Elyra (CPU)
+      description: "Elyra Notebooks | Karpenter Autoscaling"
+      kubespawner_override:
+        image: public.ecr.aws/data-on-eks/elyra-jupyter:3.15.0
+        node_selector:
+          NodePool: default
+        cpu_guarantee: 2
+        mem_guarantee: 8G
+        cpu_limit: 4
+        mem_limit: 8G
+      cmd: null
     - display_name: Data Engineering (CPU)
       description: "PySpark Notebooks | Karpenter AutoScaling"
       profile_options:
diff --git a/website/docs/blueprints/ai-ml/img/elyra-workflow.png b/website/docs/blueprints/ai-ml/img/elyra-workflow.png
new file mode 100644
index 000000000..a2f8ac6ef
Binary files /dev/null and b/website/docs/blueprints/ai-ml/img/elyra-workflow.png differ
diff --git a/website/docs/blueprints/ai-ml/img/notebook-server-list.png b/website/docs/blueprints/ai-ml/img/notebook-server-list.png
new file mode 100644
index 000000000..c1e1d5cb1
Binary files /dev/null and b/website/docs/blueprints/ai-ml/img/notebook-server-list.png differ
diff --git a/website/docs/blueprints/ai-ml/jupyterhub.md b/website/docs/blueprints/ai-ml/jupyterhub.md
index 75b7afc14..3b97237f8 100644
--- a/website/docs/blueprints/ai-ml/jupyterhub.md
+++ b/website/docs/blueprints/ai-ml/jupyterhub.md
@@ -166,9 +166,11 @@ kubectl port-forward svc/proxy-public 8080:80 -n jupyterhub
 **Sign-in:** Navigate to [http://localhost:8080/](http://localhost:8080/) in your web browser. Input `user-1` as the username and choose any password.
 ![alt text](img/image.png)
 
-Select server options: Upon sign-in, you’ll be presented with a variety of Notebook instance profiles to choose from. For this time-slicing feature demonstration, we’ll be using the **Data Science (GPU + Time-Slicing – G5)** profile. Go ahead and select this option and choose the Start button.
+Select server options: Upon sign-in, you’ll be presented with a variety of Notebook instance profiles to choose from. The `Data Engineering (CPU)` server is for traditional, CPU based notebook work. The `Elyra` server provides [Elyra](https://github.com/elyra-ai/elyra) functionality, allowing you to quickly develop pipelines: ![workflow](img/elyra-workflow.png). `Trainium` and `Inferentia` servers will deploy the notebook server onto Trainium and Inferentia nodes, allowing accelerated workloads. `Time Slicing` and `MIG` are two different strategies for GPU sharing. Finally, the `Data Science (GPU)` server is a traditional server running on an NVIDIA GPU.
 
-![alt text](img/image-1.png)
+For this time-slicing feature demonstration, we’ll be using the **Data Science (GPU + Time-Slicing – G5)** profile. Go ahead and select this option and choose the Start button.
+
+![alt text](img/notebook-server-list.png)
 
 The new node created by Karpenter with the `g5.2xlarge` instance type has been configured to leverage the timeslicing feature provided by the [NVIDIA device plugin](https://github.com/NVIDIA/k8s-device-plugin). This feature allows for efficient GPU utilization by dividing a single GPU into multiple allocatable units. In this case, we have defined `4` allocatable GPUs in the NVIDIA device plugin Helm chart config map. Below is the status of the node: