diff --git a/docs/modelserving/storage/storagecontainers.md b/docs/modelserving/storage/storagecontainers.md
index 4d4f84970..b86d884c3 100644
--- a/docs/modelserving/storage/storagecontainers.md
+++ b/docs/modelserving/storage/storagecontainers.md
@@ -36,10 +36,32 @@ In a _ClusterStorageContainer_ spec, you can specify container resource requests
 
     If a storage URI is supported by two or more _ClusterStorageContainer_ CRs, there is no guarantee which one will be used. **Please make sure that the URI format is only supported by one ClusterStorageContainer CR**.
 
+## Custom Protocol Example
 
-If you would like to use a custom protocol `model-registry://`, for example, you can create a custom image and add a new ClusterStorageContainer CR like this:
+If you would like to use a custom protocol `model-registry://`, for example, you can create a custom image and add a new `ClusterStorageContainer` CR to make it available to KServe.
 
-```yaml
+### Create the `Custom Storage Initializer` Image
+
+The first step is to create a custom container image that will be injected into the KServe deployment, as init container, and that will be in charge to download the model.
+
+The only requirement is that the `Entrypoint` of this container image should take (and properly manage) 2 positional arguments:
+1. __Source URI__: identifies the `storageUri` set in the `InferenceService`
+2. __Destination Path__: the location where the model should be stored, e.g., `/mnt/models`
+
+!!! note
+    KServe controller will take care of properly injecting your container image and invoking it with those proper arguments.
+
+A more concrete example can be found [here](https://github.com/lampajr/model-registry-storage-initializer), where the storage initializer query an existing `model registry` service in order to retrieve the original location of the model that the user requested to deploy. 
+
+###  Create the `ClusterStorageContainer` CR
+
+Once the Custom Storage Initializer image is ready, you just need to create a new `ClusterStorageContainer` CR to make it available in the cluster. You just need to provide 2 essential information:
+1. The _container spec definition_, this is strictly dependent on your own custom storage initializer image.
+2. The _supported uri formats_ for which your custom storage initializer should be injected, in this case just `model-registry://`.
+
+=== "kubectl"
+```bash
+kubectl apply -f - <<EOF
 apiVersion: "serving.kserve.io/v1alpha1"
 kind: ClusterStorageContainer
 metadata:
@@ -47,7 +69,12 @@ metadata:
 spec:
   container:
     name: storage-initializer
-    image: abc/custom-storage-initializer:latest
+    image: abc/model-registry-storage-initializer:latest
+    env:
+    - name: MODEL_REGISTRY_BASE_URL
+      value: "$MODEL_REGISTRY_SERVICE.model-registry.svc.cluster.local:$MODEL_REGISTRY_REST_PORT"
+    - name: MODEL_REGISTRY_SCHEME
+      value: "http"
     resources:
       requests:
         memory: 100Mi
@@ -57,8 +84,35 @@ spec:
         cpu: "1"
   supportedUriFormats:
     - prefix: model-registry://
+
+EOF
 ```
 
+### Deploy the Model with `InferenceService`
+
+Create the `InferenceService` with the `model-registry` specific URI format.
+
+=== "kubectl"
+```bash
+kubectl apply -n kserve-test -f - <<EOF
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: "iris-model"
+spec:
+  predictor:
+    model:
+      modelFormat:
+        name: sklearn
+      storageUri: "model-registry://iris/v1"
+EOF
+```
+
+!!! note
+    The only assumption here is that the ML model you are going to deploy has been already registered in the Model Registry, more information can be found in the [kubeflow/model-registry](https://github.com/kubeflow/model-registry) repository.
+
+In this specific example the `model-registry://iris/v1` model is referring to a registered model pointing to `gs://kfserving-examples/models/sklearn/1.0/model`. The crucial point here is that this information needs to be provided just during the registration process, whereas during every deployment action you just need to provide the `model-registry` specific URI that identifies that model (in this case `model-registry://${MODEL_NAME}/${MODEL_VERSION}`).
+
 ## Spec Attributes
 
-Spec attributes are in [API Reference](/website/reference/api/#serving.kserve.io/v1alpha1.ClusterStorageContainer) doc.
+Spec attributes are in [API Reference](/website/reference/api/#serving.kserve.io/v1alpha1.ClusterStorageContainer) doc.
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