diff --git a/docs/modelserving/storage/gcs/gcs.md b/docs/modelserving/storage/gcs/gcs.md
new file mode 100644
index 000000000..27803459f
--- /dev/null
+++ b/docs/modelserving/storage/gcs/gcs.md
@@ -0,0 +1,114 @@
+# Deploy InferenceService with a saved model on Google Cloud Storage (GCS)
+
+## Using Public GCS Bucket
+
+If no credential is provided, anonymous client will be used to download the artifact from GCS bucket.
+The uri is in the following format:
+
+
+```
+gs://${BUCKET_ NAME}/${PATH}
+```
+
+e.g. ```gs://kfserving-examples/models/tensorflow/flowers```
+
+
+## Using Private GCS bucket
+
+KServe supports authenticating using Google Service Account Key
+
+### Create a Service Account Key
+
+* To create a Service Account Key follow the steps [here](https://cloud.google.com/iam/docs/keys-create-delete#iam-service-account-keys-create-console).
+* Base64 encode the generated Service Account Key file
+
+
+## Create Google Secret
+
+### Create secret
+=== "yaml"
+```yaml
+apiVersion: v1
+kind: Secret
+metadata:
+  name: storage-config
+type: Opaque
+stringData:
+  gcs: |
+    {
+      "type": "gs",
+      "bucket": "mlpipeline",
+      "base64_service_account": "c2VydmljZWFjY291bnQ=" # base64 encoded value of the credential file
+    }
+```
+
+=== "kubectl"
+```bash
+kubectl apply -f create-gcs-secret.yaml
+```
+
+## Deploy the model on GCS with `InferenceService`
+
+Create the InferenceService with the Google service account credential
+=== "yaml"
+```yaml
+apiVersion: serving.kserve.io/v1beta1
+kind: InferenceService
+metadata:
+    name: sklearn-gcs
+spec:
+  predictor:
+      sklearn:
+        storage:
+          key: gcs
+          path: models/tensorflow/flowers
+          parameters: # Parameters to override the default values
+            bucket: kfserving-examples
+```
+
+Apply the `sklearn-gcs.yaml`.
+
+=== "kubectl"
+```bash
+kubectl apply -f sklearn-gcs.yaml
+```
+
+## Run a prediction
+
+Now, the ingress can be accessed at `${INGRESS_HOST}:${INGRESS_PORT}` or follow [this instruction](../../../get_started/first_isvc.md#4-determine-the-ingress-ip-and-ports)
+to find out the ingress IP and port.
+
+```bash
+SERVICE_HOSTNAME=$(kubectl get inferenceservice sklearn-gcs -o jsonpath='{.status.url}' | cut -d "/" -f 3)
+
+MODEL_NAME=sklearn-gcs
+INPUT_PATH=@./input.json
+curl -v -H "Host: ${SERVICE_HOSTNAME}" -H "Content-Type: application/json" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d $INPUT_PATH
+```
+
+!!! success "Expected Output"
+
+    ```{ .bash .no-copy }
+    *   Trying 127.0.0.1:8080...
+    * TCP_NODELAY set
+    * Connected to localhost (127.0.0.1) port 8080 (#0)
+    > POST /v1/models/sklearn-gcs:predict HTTP/1.1
+    > Host: sklearn-gcs.default.example.com
+    > User-Agent: curl/7.68.0
+    > Accept: */*
+    > Content-Length: 84
+    > Content-Type: application/x-www-form-urlencoded
+    >
+    * upload completely sent off: 84 out of 84 bytes
+    * Mark bundle as not supporting multiuse
+    < HTTP/1.1 200 OK
+    < content-length: 23
+    < content-type: application/json; charset=UTF-8
+    < date: Mon, 20 Sep 2021 04:55:50 GMT
+    < server: istio-envoy
+    < x-envoy-upstream-service-time: 6
+    <
+    * Connection #0 to host localhost left intact
+    {"predictions": [1, 1]}
+    ```
+
diff --git a/mkdocs.yml b/mkdocs.yml
index dcb33cc8a..ddb119a2d 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -63,6 +63,7 @@ nav:
                 - OCI: modelserving/storage/oci.md
                 - URI: modelserving/storage/uri/uri.md
                 - CA Certificate: modelserving/certificate/kserve.md
+                - GCS: modelserving/storage/gcs/gcs.md
           - Model Explainability:
                 - Concept: modelserving/explainer/explainer.md
                 - Alibi Explainer: