diff --git a/doc/source/cluster/kubernetes/getting-started.ipynb b/doc/source/cluster/kubernetes/getting-started.ipynb
index 135295219..134fca627 100644
--- a/doc/source/cluster/kubernetes/getting-started.ipynb
+++ b/doc/source/cluster/kubernetes/getting-started.ipynb
@@ -74,7 +74,7 @@
    "metadata": {},
    "source": [
     "To run the example in this guide, make sure your Kubernetes cluster (or local Kind cluster) can accomodate\n",
-    "additional resource requests of 3 CPU and 2Gi memory. \n",
+    "additional resource requests of 3 CPU and 3Gi memory. \n",
     "\n",
     "(kuberay-operator-deploy)=\n",
     "## Deploying the KubeRay operator\n",
@@ -157,7 +157,7 @@
    "outputs": [],
    "source": [
     "# Deploy a sample Ray Cluster CR from the KubeRay repo:\n",
-    "! kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/release-0.3/ray-operator/config/samples/ray-cluster.autoscaler.yaml\n",
+    "! kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-cluster.autoscaler.yaml\n",
     "\n",
     "# This Ray cluster is named `raycluster-autoscaler` because it has optional Ray Autoscaler support enabled."
    ]
diff --git a/doc/source/cluster/kubernetes/user-guides/configuring-autoscaling.md b/doc/source/cluster/kubernetes/user-guides/configuring-autoscaling.md
index ea3ab6171..3c04365df 100644
--- a/doc/source/cluster/kubernetes/user-guides/configuring-autoscaling.md
+++ b/doc/source/cluster/kubernetes/user-guides/configuring-autoscaling.md
@@ -49,7 +49,7 @@ First, follow the [quickstart guide](kuberay-quickstart) to create an autoscalin
 # Create the KubeRay operator.
 $ kubectl create -k "github.com/ray-project/kuberay/ray-operator/config/default?ref=v0.3.0&timeout=90s"
 # Create an autoscaling Ray cluster.
-$ kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/release-0.3/ray-operator/config/samples/ray-cluster.autoscaler.yaml
+$ kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-cluster.autoscaler.yaml
 ```
 
 Now, we can run a Ray program on the head pod that uses [``request_resources``](ref-autoscaler-sdk) to scale the cluster to a total of 3 CPUs. The head and worker pods in our [example cluster config](https://github.com/ray-project/kuberay/blob/master/ray-operator/config/samples/ray-cluster.autoscaler.yaml) each have a capacity of 1 CPU, and we specified a minimum of 1 worker pod. Thus, the request should trigger upscaling of one additional worker pod.