diff --git a/examples/istio/shadow/.gitignore b/examples/istio/shadow/.gitignore
new file mode 100644
index 0000000000..2b158f424d
--- /dev/null
+++ b/examples/istio/shadow/.gitignore
@@ -0,0 +1,2 @@
+model.yaml
+shadow.yaml
\ No newline at end of file
diff --git a/examples/istio/shadow/README.md b/examples/istio/shadow/README.md
new file mode 100644
index 0000000000..035a655017
--- /dev/null
+++ b/examples/istio/shadow/README.md
@@ -0,0 +1,3 @@
+# Shadow Rollout with Seldon and Istio
+
+Follow the [notebook](istio_shadow.ipynb) to show how you can deploy Shadow Seldon Deployment with Seldon and Istio. These are useful when you want to test a new model or higher latency inference piepline (e.g., with explanation components) with production traffic but without affecting the live deployment.
diff --git a/examples/istio/shadow/istio_shadow.ipynb b/examples/istio/shadow/istio_shadow.ipynb
new file mode 100644
index 0000000000..b4aa213b3d
--- /dev/null
+++ b/examples/istio/shadow/istio_shadow.ipynb
@@ -0,0 +1,477 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Shadow Rollout with Seldon and Istio\n",
+    "\n",
+    "This notebook shows how you can deploy \"shadow\" deployments to direct traffic not only to the main Seldon Deployment but also to a shadow deployment whose response will be dicarded. This allows you to test new models in a production setting and with production traffic and anlalyse how they perform before putting them live.\n",
+    "\n",
+    "These are useful when you want to test a new model or higher latency inference piepline (e.g., with explanation components) with production traffic but without affecting the live deployment.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup Seldon Core\n",
+    "\n",
+    "Use the setup notebook to [Setup Cluster](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon_core_setup.html#Setup-Cluster) with [Istio Ingress](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon_core_setup.html#Istio) and [Install Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon_core_setup.html#Install-Seldon-Core). Instructions [also online](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon_core_setup.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "namespace/seldon created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create namespace seldon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Context \"kind-kind\" modified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl config set-context $(kubectl config current-context) --namespace=seldon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.core.magic import register_line_cell_magic\n",
+    "\n",
+    "\n",
+    "@register_line_cell_magic\n",
+    "def writetemplate(line, cell):\n",
+    "    with open(line, \"w\") as f:\n",
+    "        f.write(cell.format(**globals()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ensure the istio ingress gatewaty is port-forwarded to localhost:8004\n",
+    "\n",
+    "\n",
+    "\n",
+    "* Istio: `kubectl port-forward $(kubectl get pods -l istio=ingressgateway -n istio-system -o jsonpath='{.items[0].metadata.name}') -n istio-system 8004:8080`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.7.0-dev'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ISTIO_GATEWAY = \"localhost:8004\"\n",
+    "\n",
+    "VERSION = !cat ../../../version.txt\n",
+    "VERSION = VERSION[0]\n",
+    "VERSION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Launch main model\n",
+    "\n",
+    "We will create a very simple Seldon Deployment with a dummy model image `seldonio/mock_classifier:1.0`. This deployment is named `example`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writetemplate model.yaml\n",
+    "apiVersion: machinelearning.seldon.io/v1alpha2\n",
+    "kind: SeldonDeployment\n",
+    "metadata:\n",
+    "  labels:\n",
+    "    app: seldon\n",
+    "  name: example\n",
+    "spec:\n",
+    "  name: production-model\n",
+    "  predictors:\n",
+    "  - componentSpecs:\n",
+    "    - spec:\n",
+    "        containers:\n",
+    "        - image: seldonio/mock_classifier:{VERSION}\n",
+    "          imagePullPolicy: IfNotPresent\n",
+    "          name: classifier\n",
+    "        terminationGracePeriodSeconds: 1\n",
+    "    graph:\n",
+    "      children: []\n",
+    "      endpoint:\n",
+    "        type: REST\n",
+    "      name: classifier\n",
+    "      type: MODEL\n",
+    "    name: default\n",
+    "    replicas: 1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldondeployment.machinelearning.seldon.io/example configured\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl apply -f model.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "deployment \"example-default-0-classifier\" successfully rolled out\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=example -o jsonpath='{.items[0].metadata.name}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from seldon_core.seldon_client import SeldonClient\n",
+    "\n",
+    "sc = SeldonClient(\n",
+    "    deployment_name=\"example\", namespace=\"seldon\", gateway_endpoint=ISTIO_GATEWAY\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### REST Request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success:True message:\n",
+      "Request:\n",
+      "meta {\n",
+      "}\n",
+      "data {\n",
+      "  tensor {\n",
+      "    shape: 1\n",
+      "    shape: 1\n",
+      "    values: 0.6670563912281003\n",
+      "  }\n",
+      "}\n",
+      "\n",
+      "Response:\n",
+      "{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.09538308704053941]}}, 'meta': {'requestPath': {'classifier': 'seldonio/mock_classifier:1.7.0-dev'}}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "r = sc.predict(gateway=\"istio\", transport=\"rest\")\n",
+    "assert r.success == True\n",
+    "print(r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Launch Shadow\n",
+    "\n",
+    "We will now create a new Seldon Deployment for our Shadow deployment with a new model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writetemplate shadow.yaml\n",
+    "apiVersion: machinelearning.seldon.io/v1alpha2\n",
+    "kind: SeldonDeployment\n",
+    "metadata:\n",
+    "  labels:\n",
+    "    app: seldon\n",
+    "  name: example\n",
+    "spec:\n",
+    "  name: shadow-model\n",
+    "  predictors:\n",
+    "  - componentSpecs:\n",
+    "    - spec:\n",
+    "        containers:\n",
+    "        - image: seldonio/mock_classifier:{VERSION}\n",
+    "          imagePullPolicy: IfNotPresent\n",
+    "          name: classifier\n",
+    "        terminationGracePeriodSeconds: 1\n",
+    "    graph:\n",
+    "      children: []\n",
+    "      endpoint:\n",
+    "        type: REST\n",
+    "      name: classifier\n",
+    "      type: MODEL\n",
+    "    name: default\n",
+    "    replicas: 1\n",
+    "    traffic: 100\n",
+    "  - componentSpecs:\n",
+    "    - spec:\n",
+    "        containers:\n",
+    "        - image: seldonio/mock_classifier:{VERSION}\n",
+    "          imagePullPolicy: IfNotPresent\n",
+    "          name: classifier\n",
+    "    graph:\n",
+    "      children: []\n",
+    "      endpoint:\n",
+    "        type: REST\n",
+    "      name: classifier\n",
+    "      type: MODEL\n",
+    "    name: shadow\n",
+    "    replicas: 1\n",
+    "    shadow: true\n",
+    "    traffic: 100\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "You can use the traffic field under the shadow componentSpecs to mirror a fraction of the traffic, instead of mirroring all requests. If this field is absent, all traffic will be mirrored. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldondeployment.machinelearning.seldon.io/example configured\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl apply -f shadow.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "deployment \"example-default-0-classifier\" successfully rolled out\n",
+      "Waiting for deployment \"example-shadow-0-classifier\" rollout to finish: 0 of 1 updated replicas are available...\n",
+      "deployment \"example-shadow-0-classifier\" successfully rolled out\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=example -o jsonpath='{.items[0].metadata.name}')\n",
+    "!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=example -o jsonpath='{.items[1].metadata.name}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's send a bunch of requests to the endpoint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(10):\n",
+    "    r = sc.predict(gateway=\"istio\", transport=\"rest\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "default_count = !kubectl logs $(kubectl get pod -lseldon-app=example-default -o jsonpath='{.items[0].metadata.name}') classifier | grep \"root.predict\" | wc -l"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shadow_count = !kubectl logs $(kubectl get pod -lseldon-app=example-shadow -o jsonpath='{.items[0].metadata.name}') classifier | grep \"root.predict\" | wc -l"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['10']\n",
+      "['11']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(shadow_count)\n",
+    "print(default_count)\n",
+    "assert int(shadow_count[0]) == 10\n",
+    "assert int(default_count[0]) == 11"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TearDown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldondeployment.machinelearning.seldon.io \"example\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f model.yaml"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/operator/apis/machinelearning.seldon.io/v1/seldondeployment_webhook.go b/operator/apis/machinelearning.seldon.io/v1/seldondeployment_webhook.go
index 83c5c6bfd6..da6d31e5dc 100644
--- a/operator/apis/machinelearning.seldon.io/v1/seldondeployment_webhook.go
+++ b/operator/apis/machinelearning.seldon.io/v1/seldondeployment_webhook.go
@@ -128,13 +128,17 @@ func checkTraffic(spec *SeldonDeploymentSpec, fldPath *field.Path, allErrs field
 	var shadows int = 0
 	for i := 0; i < len(spec.Predictors); i++ {
 		p := spec.Predictors[i]
-		trafficSum = trafficSum + p.Traffic
 
 		if p.Shadow == true {
 			shadows += 1
 			if shadows > 1 {
 				allErrs = append(allErrs, field.Invalid(fldPath, spec.Predictors[i].Name, "Multiple shadows are not allowed"))
 			}
+			if p.Traffic < 0 || p.Traffic > 100 {
+				allErrs = append(allErrs, field.Invalid(fldPath, spec.Predictors[i].Name, "shadow traffic is illegal, the traffic number should be between [0, 100]"))
+			}
+		} else {
+			trafficSum = trafficSum + p.Traffic
 		}
 	}
 
diff --git a/operator/apis/machinelearning.seldon.io/v1/seldondeployment_webhook_test.go b/operator/apis/machinelearning.seldon.io/v1/seldondeployment_webhook_test.go
index d2cb2bf6fb..1f122d9259 100644
--- a/operator/apis/machinelearning.seldon.io/v1/seldondeployment_webhook_test.go
+++ b/operator/apis/machinelearning.seldon.io/v1/seldondeployment_webhook_test.go
@@ -1384,3 +1384,37 @@ func TestValidateTwoShadows(t *testing.T) {
 	err = spec.ValidateSeldonDeployment()
 	g.Expect(err).ToNot(BeNil())
 }
+
+func TestValidateShadowTraffic(t *testing.T) {
+	g := NewGomegaWithT(t)
+	err := setupTestConfigMap()
+	g.Expect(err).To(BeNil())
+	impl := PredictiveUnitImplementation(constants.PrePackedServerTensorflow)
+	spec := &SeldonDeploymentSpec{
+		Protocol: ProtocolTensorflow,
+		Predictors: []PredictorSpec{
+			{
+				Name: "p1",
+				Graph: PredictiveUnit{
+					Name:           "classifier",
+					Implementation: &impl,
+					ModelURI:       "s3://mybucket/model",
+				},
+				Traffic: 100,
+			},
+			{
+				Name: "p2",
+				Graph: PredictiveUnit{
+					Name:           "classifier",
+					Implementation: &impl,
+					ModelURI:       "s3://mybucket/model",
+				},
+				Shadow:  true,
+				Traffic: 101,
+			},
+		},
+	}
+	spec.DefaultSeldonDeployment("mydep", "default")
+	err = spec.ValidateSeldonDeployment()
+	g.Expect(err).ToNot(BeNil())
+}
diff --git a/operator/controllers/seldondeployment_controller.go b/operator/controllers/seldondeployment_controller.go
index a5014c4142..7717676061 100644
--- a/operator/controllers/seldondeployment_controller.go
+++ b/operator/controllers/seldondeployment_controller.go
@@ -327,6 +327,17 @@ func createIstioResources(mlDep *machinelearningv1.SeldonDeployment,
 				},
 			}
 
+			if p.Traffic > 0 {
+				//if shadow predictor's traffic is greater than 0, set the mirror percentage (like https://istio.io/latest/docs/tasks/traffic-management/mirroring/#mirroring-traffic-to-v2) in VirtualService
+				vsvc.Spec.Http[0].MirrorPercentage = &istio_networking.Percent{
+					Value: float64(p.Traffic),
+				}
+
+				vsvc.Spec.Http[1].MirrorPercentage = &istio_networking.Percent{
+					Value: float64(p.Traffic),
+				}
+			}
+
 			continue
 		}