diff --git a/doc/source/ray-overview/getting-started.md b/doc/source/ray-overview/getting-started.md
index c017bece99c2e..6e8d456c26f49 100644
--- a/doc/source/ray-overview/getting-started.md
+++ b/doc/source/ray-overview/getting-started.md
@@ -530,12 +530,24 @@ Learn more about Ray Core
 
 ## Ray Cluster Quickstart
 
-Deploy your applications on Ray clusters, often with minimal code changes to your existing code.
+Deploy your applications on Ray clusters on AWS, GCP, Azure, and more, often with minimal code changes to your existing code.
+
 
 `````{dropdown} <img src="images/ray_svg_logo.svg" alt="ray" width="50px"> Clusters: Launching a Ray Cluster on AWS
 :animate: fade-in-slide-down
 
 Ray programs can run on a single machine, or seamlessly scale to large clusters.
+
+:::{note}
+To run this example install the following:
+
+```bash
+pip install -U "ray[default]" boto3
+```
+
+If you haven't already, configure your credentials as described in the https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#guide-credentials[documentation for boto3].
+:::
+
 Take this simple example that waits for individual nodes to join the cluster.
 
 ````{dropdown} example.py
@@ -545,16 +557,16 @@ Take this simple example that waits for individual nodes to join the cluster.
 :language: python
 ```
 ````
-You can also download this example from our [GitHub repository](https://github.com/ray-project/ray/blob/master/doc/yarn/example.py).
-Go ahead and store it locally in a file called `example.py`.
+You can also download this example from the [GitHub repository](https://github.com/ray-project/ray/blob/master/doc/yarn/example.py).
+Store it locally in a file called `example.py`.
 
-To execute this script in the cloud, just download [this configuration file](https://github.com/ray-project/ray/blob/master/python/ray/autoscaler/aws/example-full.yaml),
+To execute this script in the cloud, download [this configuration file](https://github.com/ray-project/ray/blob/master/python/ray/autoscaler/aws/example-minimal.yaml),
 or copy it here:
 
 ````{dropdown} cluster.yaml
 :animate: fade-in-slide-down
 
-```{literalinclude} ../../../python/ray/autoscaler/aws/example-full.yaml
+```{literalinclude} ../../../python/ray/autoscaler/aws/example-minimal.yaml
 :language: yaml
 ```
 ````
@@ -570,7 +582,7 @@ ray submit cluster.yaml example.py --start
 :outline:
 :expand:
 
-Learn more about launching Ray Clusters
+Learn more about launching Ray Clusters on AWS, GCP, Azure, and more
 ```
 
 `````
diff --git a/python/ray/autoscaler/aws/example-minimal.yaml b/python/ray/autoscaler/aws/example-minimal.yaml
index 09a2727d1311c..c230d37673af4 100644
--- a/python/ray/autoscaler/aws/example-minimal.yaml
+++ b/python/ray/autoscaler/aws/example-minimal.yaml
@@ -5,3 +5,44 @@ cluster_name: aws-example-minimal
 provider:
     type: aws
     region: us-west-2
+
+# The maximum number of workers nodes to launch in addition to the head
+# node.
+max_workers: 3
+
+# Tell the autoscaler the allowed node types and the resources they provide.
+# The key is the name of the node type, which is for debugging purposes.
+# The node config specifies the launch config and physical instance type.
+available_node_types:
+    ray.head.default:
+        # The node type's CPU and GPU resources are auto-detected based on AWS instance type.
+        # If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler.
+        # You can also set custom resources.
+        # For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called "custom", set
+        # resources: {"CPU": 1, "GPU": 1, "custom": 5}
+        resources: {}
+        # Provider-specific config for this node type, e.g., instance type. By default
+        # Ray auto-configures unspecified fields such as SubnetId and KeyName.
+        # For more documentation on available fields, see
+        # http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
+        node_config:
+            InstanceType: m5.large
+    ray.worker.default:
+        # The minimum number of worker nodes of this type to launch.
+        # This number should be >= 0.
+        min_workers: 3
+        # The maximum number of worker nodes of this type to launch.
+        # This parameter takes precedence over min_workers.
+        max_workers: 3
+        # The node type's CPU and GPU resources are auto-detected based on AWS instance type.
+        # If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler.
+        # You can also set custom resources.
+        # For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called "custom", set
+        # resources: {"CPU": 1, "GPU": 1, "custom": 5}
+        resources: {}
+        # Provider-specific config for this node type, e.g., instance type. By default
+        # Ray auto-configures unspecified fields such as SubnetId and KeyName.
+        # For more documentation on available fields, see
+        # http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
+        node_config:
+            InstanceType: m5.large