diff --git a/examples/models/deep_mnist/README.md b/examples/models/deep_mnist/README.md
deleted file mode 100644
index 635bc5438b..0000000000
--- a/examples/models/deep_mnist/README.md
+++ /dev/null
@@ -1,90 +0,0 @@
-# Tensorflow MNIST
-A TensorFlow model for MNIST classification.
-
-## Dependencies
-
-```bash
-pip install tensorflow
-```
-
-## Train locally
-
-```bash
-make train
-```
-
-## Wrap using [s2i](https://github.com/openshift/source-to-image#installation).
-
-```bash
-s2i build . seldonio/seldon-core-s2i-python2 deep-mnist
-```
-
-## Local Docker Smoke Test
-
-Run under docker.
-
-```bash
-docker run --rm -p 5000:5000 deep-mnist
-```
-
-Ensure test grpc modules compiled.
-
-```bash
-pushd ../../../wrappers/testing ; make build_protos ; popd
-```
-
-Send a data request using the wrapper tester.
-
-```bash
-python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p
-```
-
-## Minikube test
-
-```bash
-minikube start --memory 4096
-```
-
-Install seldon core
-
-```
-helm install seldon-core-crd --name seldon-core-crd --repo https://storage.googleapis.com/seldon-charts --set usage_metrics.enabled=true --set rbac.enabled=false
-helm install seldon-core --name seldon-core --repo https://storage.googleapis.com/seldon-charts --set rbac.enabled=false
-```
-
-Connect to Minikube Docker daemon
-
-```bash
-eval $(minikube docker-env)
-```
-
-Build image using minikube docker daemon.
-
-```bash
-s2i build . seldonio/seldon-core-s2i-python2 deep-mnist
-```
-
-Launch deployment
-
-```bash
-kubectl create -f deep_mnist.json
-```
-
-Port forward API server
-
-```bash
-kubectl port-forward $(kubectl get pods -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].metadata.name}') 8080:8080
-```
-
-Ensure tester gRPC modules compiled. You will need to install grpc-tools module.
-
-```bash
-pushd ../../../util/api_tester ; make build_protos ; popd
-```
-
-Send test request
-```bash
-python ../../../util/api_tester/api-tester.py contract.json 0.0.0.0 8080 --oauth-key oauth-key --oauth-secret oauth-secret -p
-```
-
-
diff --git a/examples/models/deep_mnist/deep_mnist.ipynb b/examples/models/deep_mnist/deep_mnist.ipynb
new file mode 100644
index 0000000000..9801bbaa55
--- /dev/null
+++ b/examples/models/deep_mnist/deep_mnist.ipynb
@@ -0,0 +1,261 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tensorflow MNIST Model Deployment\n",
+    "\n",
+    " * Wrap a Tensorflow MNIST python model for use as a prediction microservice in seldon-core\n",
+    "   * Run locally on Docker to test\n",
+    "   * Deploy on seldon-core running on minikube\n",
+    " \n",
+    "## Depenencies\n",
+    "\n",
+    " * [Helm](https://github.com/kubernetes/helm)\n",
+    " * [Minikube](https://github.com/kubernetes/minikube)\n",
+    " * [S2I](https://github.com/openshift/source-to-image)\n",
+    "\n",
+    "```bash\n",
+    "pip install tensorflow\n",
+    "pip install grpcio-tools\n",
+    "```\n",
+    "\n",
+    "## Train locally\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.examples.tutorials.mnist import input_data\n",
+    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot = True)\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "    \n",
+    "    x = tf.placeholder(tf.float32, [None,784], name=\"x\")\n",
+    "\n",
+    "    W = tf.Variable(tf.zeros([784,10]))\n",
+    "    b = tf.Variable(tf.zeros([10]))\n",
+    "\n",
+    "    y = tf.nn.softmax(tf.matmul(x,W) + b, name=\"y\")\n",
+    "\n",
+    "    y_ = tf.placeholder(tf.float32, [None, 10])\n",
+    "\n",
+    "\n",
+    "    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))\n",
+    "\n",
+    "    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n",
+    "\n",
+    "    init = tf.initialize_all_variables()\n",
+    "\n",
+    "    sess = tf.Session()\n",
+    "    sess.run(init)\n",
+    "\n",
+    "    for i in range(1000):\n",
+    "        batch_xs, batch_ys = mnist.train.next_batch(100)\n",
+    "        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n",
+    "\n",
+    "    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n",
+    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
+    "    print(sess.run(accuracy, feed_dict = {x: mnist.test.images, y_:mnist.test.labels}))\n",
+    "\n",
+    "    saver = tf.train.Saver()\n",
+    "\n",
+    "    saver.save(sess, \"model/deep_mnist_model\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wrap model using s2i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!s2i build . seldonio/seldon-core-s2i-python2 deep-mnist:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker run --name \"mnist_predictor\" -d --rm -p 5000:5000 deep-mnist:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../../../wrappers/testing && make build_protos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Send some random features that conform to the contract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker rm mnist_predictor --force"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube start --memory 4096 --feature-gates=CustomResourceValidation=true --extra-config=apiserver.Authorization.Mode=RBAC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm install ../../../helm-charts/seldon-core-crd --name seldon-core-crd  --set usage_metrics.enabled=true\n",
+    "!helm install ../../../helm-charts/seldon-core --name seldon-core "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!eval $(minikube docker-env) && s2i build . seldonio/seldon-core-s2i-python2 deep-mnist:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create -f deep_mnist.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wait until ready (replicas == replicasAvailable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl get seldondeployments deep-mnist -o jsonpath='{.status}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../../../util/api_tester && make build_protos "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../../util/api_tester/api-tester.py contract.json \\\n",
+    "    `minikube ip` `kubectl get svc -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].spec.ports[0].nodePort}'` \\\n",
+    "    --oauth-key oauth-key --oauth-secret oauth-secret -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube delete"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/models/deep_mnist/deep_mnist.json b/examples/models/deep_mnist/deep_mnist.json
index 3b95f90816..44710cc199 100644
--- a/examples/models/deep_mnist/deep_mnist.json
+++ b/examples/models/deep_mnist/deep_mnist.json
@@ -21,7 +21,7 @@
                     "spec": {
                         "containers": [
                             {
-                                "image": "deep-mnist:latest",
+                                "image": "deep-mnist:0.1",
                                 "imagePullPolicy": "IfNotPresent",
                                 "name": "classifier",
                                 "resources": {
diff --git a/examples/models/h2o_mojo/h2o_model.ipynb b/examples/models/h2o_mojo/h2o_model.ipynb
new file mode 100644
index 0000000000..5a010c02d1
--- /dev/null
+++ b/examples/models/h2o_mojo/h2o_model.ipynb
@@ -0,0 +1,255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# H2O Model\n",
+    "\n",
+    " * Wrap a H2O model for use as a prediction microservice in seldon-core\n",
+    "   * Run locally on Docker to test\n",
+    "   * Deploy on seldon-core running on minikube\n",
+    " \n",
+    "## Depenencies\n",
+    "\n",
+    " * [Helm](https://github.com/kubernetes/helm)\n",
+    " * [Minikube](https://github.com/kubernetes/minikube)\n",
+    " * [S2I](https://github.com/openshift/source-to-image)\n",
+    " * [H2O](https://www.h2o.ai/download/)\n",
+    "\n",
+    "```bash\n",
+    "pip install sklearn\n",
+    "pip install grpcio-tools\n",
+    "```\n",
+    "\n",
+    "## Train locally\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mkdir -p experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h2o\n",
+    "h2o.init()\n",
+    "from h2o.estimators.glm import H2OGeneralizedLinearEstimator\n",
+    "path = \"http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip\"\n",
+    "h2o_df = h2o.import_file(path)\n",
+    "h2o_df['CAPSULE'] = h2o_df['CAPSULE'].asfactor()\n",
+    "model = H2OGeneralizedLinearEstimator(family = \"binomial\")\n",
+    "model.train(y = \"CAPSULE\",\n",
+    "            x = [\"AGE\", \"RACE\", \"PSA\", \"GLEASON\"],\n",
+    "            training_frame = h2o_df)\n",
+    "modelfile = model.download_mojo(path=\"./experiment/\", get_genmodel_jar=False)\n",
+    "print(\"Model saved to \" + modelfile)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mv experiment/*.zip src/main/resources/model.zip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wrap model using s2i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!s2i build . seldonio/seldon-core-s2i-java-build h2o-test:0.1 --runtime-image seldonio/seldon-core-s2i-java-runtime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker run --name \"h2o_predictor\" -d --rm -p 5000:5000 h2o-test:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../../../wrappers/testing && make build_protos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Send some random features that conform to the contract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker rm h2o_predictor --force"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube start --memory 4096 --feature-gates=CustomResourceValidation=true --extra-config=apiserver.Authorization.Mode=RBAC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm install ../../../helm-charts/seldon-core-crd --name seldon-core-crd  --set usage_metrics.enabled=true\n",
+    "!helm install ../../../helm-charts/seldon-core --name seldon-core "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!eval $(minikube docker-env) && s2i build . seldonio/seldon-core-s2i-java-build h2o-test:0.1 --runtime-image seldonio/seldon-core-s2i-java-runtime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create -f h2o_deployment.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wait until ready (replicas == replicasAvailable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl get seldondeployments seldon-deployment-example -o jsonpath='{.status}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../../../util/api_tester && make build_protos "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../../util/api_tester/api-tester.py contract.json \\\n",
+    "    `minikube ip` `kubectl get svc -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].spec.ports[0].nodePort}'` \\\n",
+    "    --oauth-key oauth-key --oauth-secret oauth-secret -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube delete"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/models/keras_mnist/README.md b/examples/models/keras_mnist/README.md
deleted file mode 100644
index b632fdf243..0000000000
--- a/examples/models/keras_mnist/README.md
+++ /dev/null
@@ -1,91 +0,0 @@
-# Deep MNIST
-A Keras model for MNIST classification.
-
-## Depenencies
-
-```bash
-pip install keras
-pip install grpcio-tools
-```
-
-## Train locally
-
-```bash
-python train_mnist.py
-```
-
-## Wrap using [s2i](https://github.com/openshift/source-to-image#installation).
-
-```bash
-s2i build . seldonio/seldon-core-s2i-python3 keras-mnist:0.1
-```
-
-## Local Docker Smoke Test
-
-Run under docker.
-
-```bash
-docker run --rm -p 5000:5000 keras-mnist:0.1
-```
-
-Ensure test grpc modules compiled.
-
-```bash
-pushd ../../../wrappers/testing ; make build_protos ; popd
-```
-
-Send a data request using the wrapper tester.
-
-```bash
-python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p
-```
-
-## Minikube test
-
-```bash
-minikube start --memory 4096
-```
-
-Install seldon core
-
-```
-helm install seldon-core-crd --name seldon-core-crd --repo https://storage.googleapis.com/seldon-charts --set usage_metrics.enabled=true --set rbac.enabled=false
-helm install seldon-core --name seldon-core --repo https://storage.googleapis.com/seldon-charts --set rbac.enabled=false
-```
-
-Connect to Minikube Docker daemon
-
-```bash
-eval $(minikube docker-env)
-```
-
-Build image using minikube docker daemon.
-
-```bash
-s2i build . seldonio/seldon-core-s2i-python3 keras-mnist:0.1
-```
-
-Launch deployment
-
-```bash
-kubectl create -f keras_mnist_deployment.json
-```
-
-Port forward API server
-
-```bash
-kubectl port-forward $(kubectl get pods -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].metadata.name}') 8080:8080
-```
-
-Ensure tester gRPC modules compiled. 
-
-```bash
-pushd ../../../util/api_tester ; make build_protos ; popd
-```
-
-Send test request
-```bash
-python ../../../util/api_tester/api-tester.py contract.json 0.0.0.0 8080 --oauth-key oauth-key --oauth-secret oauth-secret -p
-```
-
-
diff --git a/examples/models/keras_mnist/keras_mnist.ipynb b/examples/models/keras_mnist/keras_mnist.ipynb
new file mode 100644
index 0000000000..13d6e978af
--- /dev/null
+++ b/examples/models/keras_mnist/keras_mnist.ipynb
@@ -0,0 +1,429 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Keras MNIST Model Deployment\n",
+    "\n",
+    " * Wrap a Tensorflow MNIST python model for use as a prediction microservice in seldon-core\n",
+    "   * Run locally on Docker to test\n",
+    "   * Deploy on seldon-core running on minikube\n",
+    " \n",
+    "## Depenencies\n",
+    "\n",
+    " * [Helm](https://github.com/kubernetes/helm)\n",
+    " * [Minikube](https://github.com/kubernetes/minikube)\n",
+    " * [S2I](https://github.com/openshift/source-to-image)\n",
+    "\n",
+    "```bash\n",
+    "pip install keras\n",
+    "pip install grpcio-tools\n",
+    "```\n",
+    "\n",
+    "## Train locally\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import math\n",
+    "import datetime\n",
+    "#from seldon.pipeline import PipelineSaver\n",
+    "import os\n",
+    "import tensorflow as tf\n",
+    "from keras import backend\n",
+    "from keras.models import Model,load_model\n",
+    "from keras.layers import Dense,Input\n",
+    "from keras.layers import Dropout\n",
+    "from keras.layers import Flatten, Reshape\n",
+    "from keras.constraints import maxnorm\n",
+    "from keras.layers.convolutional import Convolution2D\n",
+    "from keras.layers.convolutional import MaxPooling2D\n",
+    "\n",
+    "from keras.callbacks import TensorBoard\n",
+    "\n",
+    "class MnistFfnn(object):\n",
+    "\n",
+    "    def __init__(self,\n",
+    "                 input_shape=(784,),\n",
+    "                 nb_labels=10,\n",
+    "                 optimizer='Adam',\n",
+    "                 run_dir='tensorboardlogs_test'):\n",
+    "        \n",
+    "        self.model_name='MnistFfnn'\n",
+    "        self.run_dir=run_dir\n",
+    "        self.input_shape=input_shape\n",
+    "        self.nb_labels=nb_labels\n",
+    "        self.optimizer=optimizer\n",
+    "        self.build_graph()\n",
+    "\n",
+    "    def build_graph(self):\n",
+    "                            \n",
+    "        inp = Input(shape=self.input_shape,name='input_part')\n",
+    "\n",
+    "        #keras layers\n",
+    "        with tf.name_scope('dense_1') as scope:\n",
+    "            h1 = Dense(256,\n",
+    "                         activation='relu',\n",
+    "                         W_constraint=maxnorm(3))(inp)\n",
+    "            drop1 = Dropout(0.2)(h1)\n",
+    "\n",
+    "        with tf.name_scope('dense_2') as scope:\n",
+    "            h2 = Dense(128,\n",
+    "                       activation='relu',\n",
+    "                       W_constraint=maxnorm(3))(drop1)\n",
+    "            drop2 = Dropout(0.5)(h2)\n",
+    "            \n",
+    "            out = Dense(self.nb_labels,\n",
+    "                        activation='softmax')(drop2)\n",
+    "\n",
+    "        self.model = Model(inp,out)\n",
+    "        \n",
+    "        if self.optimizer ==  'rmsprop':\n",
+    "            self.model.compile(loss='categorical_crossentropy',\n",
+    "                               optimizer='rmsprop',\n",
+    "                               metrics=['accuracy'])\n",
+    "        elif self.optimizer == 'Adam':\n",
+    "            self.model.compile(loss='categorical_crossentropy',\n",
+    "                               optimizer='Adam',\n",
+    "                               metrics=['accuracy'])\n",
+    "            \n",
+    "        print('graph builded')\n",
+    "\n",
+    "    def fit(self,X,y=None,\n",
+    "            X_test=None,y_test=None,\n",
+    "            batch_size=128,\n",
+    "            nb_epochs=2,\n",
+    "            shuffle=True):\n",
+    "        \n",
+    "        now = datetime.datetime.now()\n",
+    "        tensorboard_logname = self.run_dir+'/{}_{}'.format(self.model_name,\n",
+    "                                                           now.strftime('%Y.%m.%d_%H.%M'))      \n",
+    "        tensorboard = TensorBoard(log_dir=tensorboard_logname)\n",
+    "        \n",
+    "        self.model.fit(X,y,\n",
+    "                       validation_data=(X_test,y_test),\n",
+    "                       callbacks=[tensorboard],\n",
+    "                       batch_size=batch_size, \n",
+    "                       nb_epoch=nb_epochs,\n",
+    "                       shuffle = shuffle)\n",
+    "        return self\n",
+    "    \n",
+    "    def predict_proba(self,X):\n",
+    "\n",
+    "        return self.model.predict_proba(X)\n",
+    "    \n",
+    "    def predict(self, X):\n",
+    "        probas = self.model.predict_proba(X)\n",
+    "        return([[p>0.5 for p in p1] for p1 in probas])\n",
+    "        \n",
+    "    def score(self, X, y=None):\n",
+    "        pass\n",
+    "\n",
+    "    def get_class_id_map(self):\n",
+    "        return [\"proba\"]\n",
+    "\n",
+    "class MnistConv(object):\n",
+    "\n",
+    "    def __init__(self,\n",
+    "                 input_shape=(784,),\n",
+    "                 nb_labels=10,\n",
+    "                 optimizer='Adam',\n",
+    "                 run_dir='tensorboardlogs_test',\n",
+    "                 saved_model_file='MnistClassifier.h5'):\n",
+    "        \n",
+    "        self.model_name='MnistConv'\n",
+    "        self.run_dir=run_dir\n",
+    "        self.input_shape=input_shape\n",
+    "        self.nb_labels=nb_labels\n",
+    "        self.optimizer=optimizer\n",
+    "        self.saved_model_file=saved_model_file\n",
+    "        self.build_graph()\n",
+    "\n",
+    "    def build_graph(self):\n",
+    "                                                                \n",
+    "        inp = Input(shape=self.input_shape,name='input_part')\n",
+    "        inp2 = Reshape((28,28,1))(inp)      \n",
+    "        #keras layers\n",
+    "        with tf.name_scope('conv') as scope:\n",
+    "            conv = Convolution2D(32, 3, 3,\n",
+    "                                 input_shape=(32, 32, 3),\n",
+    "                                 border_mode='same',\n",
+    "                                 activation='relu',\n",
+    "                                 W_constraint=maxnorm(3))(inp2)\n",
+    "            drop_conv = Dropout(0.2)(conv)\n",
+    "            max_pool = MaxPooling2D(pool_size=(2, 2))(drop_conv)\n",
+    "\n",
+    "        with tf.name_scope('dense') as scope:\n",
+    "            flat = Flatten()(max_pool)                \n",
+    "            dense = Dense(128,\n",
+    "                          activation='relu',\n",
+    "                          W_constraint=maxnorm(3))(flat)\n",
+    "            drop_dense = Dropout(0.5)(dense)\n",
+    "            \n",
+    "            out = Dense(self.nb_labels,\n",
+    "                        activation='softmax')(drop_dense)\n",
+    "\n",
+    "        self.model = Model(inp,out)\n",
+    "        \n",
+    "        if self.optimizer ==  'rmsprop':\n",
+    "            self.model.compile(loss='categorical_crossentropy',\n",
+    "                               optimizer='rmsprop',\n",
+    "                               metrics=['accuracy'])\n",
+    "        elif self.optimizer == 'Adam':\n",
+    "            self.model.compile(loss='categorical_crossentropy',\n",
+    "                               optimizer='Adam',\n",
+    "                               metrics=['accuracy'])\n",
+    "            \n",
+    "        print('graph builded')\n",
+    "\n",
+    "    def fit(self,X,y=None,\n",
+    "            X_test=None,y_test=None,\n",
+    "            batch_size=128,\n",
+    "            nb_epochs=2,\n",
+    "            shuffle=True):\n",
+    "        \n",
+    "        now = datetime.datetime.now()\n",
+    "        tensorboard_logname = self.run_dir+'/{}_{}'.format(self.model_name,\n",
+    "                                                           now.strftime('%Y.%m.%d_%H.%M'))      \n",
+    "        tensorboard = TensorBoard(log_dir=tensorboard_logname)\n",
+    "        \n",
+    "        self.model.fit(X,y,\n",
+    "                       validation_data=(X_test,y_test),\n",
+    "                       callbacks=[tensorboard],\n",
+    "                       batch_size=batch_size, \n",
+    "                       nb_epoch=nb_epochs,\n",
+    "                       shuffle = shuffle)\n",
+    "        #if not os.path.exists('saved_model'):\n",
+    "        #    os.makedirs('saved_model')\n",
+    "        self.model.save(self.saved_model_file)\n",
+    "        return self\n",
+    "    \n",
+    "    def predict_proba(self,X):\n",
+    "        return self.model.predict_proba(X)\n",
+    "    \n",
+    "    def predict(self, X):\n",
+    "        probas = self.model.predict_proba(X)\n",
+    "        return([[p>0.5 for p in p1] for p1 in probas])\n",
+    "        \n",
+    "    def score(self, X, y=None):\n",
+    "        pass\n",
+    "\n",
+    "    def get_class_id_map(self):\n",
+    "        return [\"proba\"]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.examples.tutorials.mnist import input_data\n",
+    "mnist = input_data.read_data_sets('data/MNIST_data', one_hot=True)\n",
+    "X_train = mnist.train.images\n",
+    "y_train = mnist.train.labels\n",
+    "X_test = mnist.test.images\n",
+    "y_test = mnist.test.labels\n",
+    "mc = MnistConv()\n",
+    "mc.fit(X_train,y=y_train,\n",
+    "    X_test=X_test,y_test=y_test)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wrap model using s2i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!s2i build . seldonio/seldon-core-s2i-python3 keras-mnist:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker run --name \"mnist_predictor\" -d --rm -p 5000:5000 keras-mnist:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../../../wrappers/testing && make build_protos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Send some random features that conform to the contract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker rm mnist_predictor --force"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube start --memory 4096 --feature-gates=CustomResourceValidation=true --extra-config=apiserver.Authorization.Mode=RBAC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm install ../../../helm-charts/seldon-core-crd --name seldon-core-crd  --set usage_metrics.enabled=true\n",
+    "!helm install ../../../helm-charts/seldon-core --name seldon-core "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!eval $(minikube docker-env) && s2i build . seldonio/seldon-core-s2i-python3 keras-mnist:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create -f keras_mnist_deployment.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wait until ready (replicas == replicasAvailable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl get seldondeployments seldon-deployment-example -o jsonpath='{.status}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../../../util/api_tester && make build_protos "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../../util/api_tester/api-tester.py contract.json \\\n",
+    "    `minikube ip` `kubectl get svc -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].spec.ports[0].nodePort}'` \\\n",
+    "    --oauth-key oauth-key --oauth-secret oauth-secret -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube delete"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/models/r_iris/README.md b/examples/models/r_iris/README.md
deleted file mode 100644
index 6567591e37..0000000000
--- a/examples/models/r_iris/README.md
+++ /dev/null
@@ -1,88 +0,0 @@
-# R model for IRIS dataset
-An R Iris model.
-
-## Depenencies
-
-R
-
-## Train locally
-
-```bash
-Rscript train.R
-```
-
-## Wrap using [s2i](https://github.com/openshift/source-to-image#installation).
-
-```bash
-s2i build . seldonio/seldon-core-s2i-r r-iris:0.1
-```
-
-## Local Docker Smoke Test
-
-Run under docker.
-
-```bash
-docker run --rm -p 5000:5000 r-iris:0.1
-```
-
-Ensure test grpc modules compiled.
-
-```bash
-pushd ../../../wrappers/testing ; make build_protos ; popd
-```
-
-Send a data request using the wrapper tester.
-
-```bash
-python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p
-```
-
-## Minikube test
-
-```bash
-minikube start --memory 4096
-```
-
-Install seldon core
-
-```
-helm install seldon-core-crd --name seldon-core-crd --repo https://storage.googleapis.com/seldon-charts --set usage_metrics.enabled=true --set rbac.enabled=false
-helm install seldon-core --name seldon-core --repo https://storage.googleapis.com/seldon-charts --set rbac.enabled=false
-```
-
-Connect to Minikube Docker daemon
-
-```bash
-eval $(minikube docker-env)
-```
-
-Build image using minikube docker daemon.
-
-```bash
-s2i build . seldonio/seldon-core-s2i-r r-iris:0.1
-```
-
-Launch deployment
-
-```bash
-kubectl create -f r_iris_deployment.json
-```
-
-Port forward API server
-
-```bash
-kubectl port-forward $(kubectl get pods -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].metadata.name}') 8080:8080
-```
-
-Ensure tester gRPC modules compiled. 
-
-```bash
-pushd ../../../util/api_tester ; make build_protos ; popd
-```
-
-Send test request
-```bash
-python ../../../util/api_tester/api-tester.py contract.json 0.0.0.0 8080 --oauth-key oauth-key --oauth-secret oauth-secret -p
-```
-
-
diff --git a/examples/models/r_iris/r_iris.ipynb b/examples/models/r_iris/r_iris.ipynb
new file mode 100644
index 0000000000..1b21d084d6
--- /dev/null
+++ b/examples/models/r_iris/r_iris.ipynb
@@ -0,0 +1,226 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# R IRIS Model\n",
+    "\n",
+    " * Wrap a scikit-learn python model for use as a prediction microservice in seldon-core\n",
+    "   * Run locally on Docker to test\n",
+    "   * Deploy on seldon-core running on minikube\n",
+    " \n",
+    "## Depenencies\n",
+    "\n",
+    " * [Helm](https://github.com/kubernetes/helm)\n",
+    " * [Minikube](https://github.com/kubernetes/minikube)\n",
+    " * [S2I](https://github.com/openshift/source-to-image)\n",
+    " * R\n",
+    "\n",
+    "```bash\n",
+    "pip install sklearn\n",
+    "pip install grpcio-tools\n",
+    "```\n",
+    "\n",
+    "## Train locally\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!Rscript train.R"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wrap model using s2i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!s2i build . seldonio/seldon-core-s2i-r r-iris:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker run --name \"iris_predictor\" -d --rm -p 5000:5000 r-iris:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../../../wrappers/testing && make build_protos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Send some random features that conform to the contract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker rm iris_predictor --force"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube start --memory 4096 --feature-gates=CustomResourceValidation=true --extra-config=apiserver.Authorization.Mode=RBAC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm install ../../../helm-charts/seldon-core-crd --name seldon-core-crd  --set usage_metrics.enabled=true\n",
+    "!helm install ../../../helm-charts/seldon-core --name seldon-core "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!eval $(minikube docker-env) && s2i build . seldonio/seldon-core-s2i-r r-iris:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create -f r_iris_deployment.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wait until ready (replicas == replicasAvailable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl get seldondeployments seldon-deployment-example -o jsonpath='{.status}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../../../util/api_tester && make build_protos "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../../util/api_tester/api-tester.py contract.json \\\n",
+    "    `minikube ip` `kubectl get svc -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].spec.ports[0].nodePort}'` \\\n",
+    "    --oauth-key oauth-key --oauth-secret oauth-secret -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube delete"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/models/r_mnist/README.md b/examples/models/r_mnist/README.md
deleted file mode 100644
index bedd582973..0000000000
--- a/examples/models/r_mnist/README.md
+++ /dev/null
@@ -1,88 +0,0 @@
-# Deep MNIST
-An R MNIST model.
-
-## Depenencies
-
-R
-
-## Train locally
-
-```bash
-make train
-```
-
-## Wrap using [s2i](https://github.com/openshift/source-to-image#installation).
-
-```bash
-s2i build . seldonio/seldon-core-s2i-r r-mnist:0.1
-```
-
-## Local Docker Smoke Test
-
-Run under docker.
-
-```bash
-docker run --rm -p 5000:5000 r-mnist:0.1
-```
-
-Ensure test grpc modules compiled.
-
-```bash
-pushd ../../../wrappers/testing ; make build_protos ; popd
-```
-
-Send a data request using the wrapper tester.
-
-```bash
-python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p
-```
-
-## Minikube test
-
-```bash
-minikube start --memory 4096
-```
-
-Install seldon core
-
-```
-helm install seldon-core-crd --name seldon-core-crd --repo https://storage.googleapis.com/seldon-charts --set usage_metrics.enabled=true --set rbac.enabled=false
-helm install seldon-core --name seldon-core --repo https://storage.googleapis.com/seldon-charts --set rbac.enabled=false
-```
-
-Connect to Minikube Docker daemon
-
-```bash
-eval $(minikube docker-env)
-```
-
-Build image using minikube docker daemon.
-
-```bash
-s2i build . seldonio/seldon-core-s2i-r r-mnist:0.1
-```
-
-Launch deployment
-
-```bash
-kubectl create -f r_mnist_deployment.json -n seldon
-```
-
-Port forward API server
-
-```bash
-kubectl port-forward $(kubectl get pods -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].metadata.name}') 8080:8080
-```
-
-Ensure tester gRPC modules compiled. 
-
-```bash
-pushd ../../../util/api_tester ; make build_protos ; popd
-```
-
-Send test request
-```bash
-python ../../../util/api_tester/api-tester.py contract.json 0.0.0.0 8080 --oauth-key oauth-key --oauth-secret oauth-secret -p
-```
-
-
diff --git a/examples/models/r_mnist/r_mnist.ipynb b/examples/models/r_mnist/r_mnist.ipynb
new file mode 100644
index 0000000000..4d9229425b
--- /dev/null
+++ b/examples/models/r_mnist/r_mnist.ipynb
@@ -0,0 +1,226 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# R MNIST Model\n",
+    "\n",
+    " * Wrap a scikit-learn python model for use as a prediction microservice in seldon-core\n",
+    "   * Run locally on Docker to test\n",
+    "   * Deploy on seldon-core running on minikube\n",
+    " \n",
+    "## Depenencies\n",
+    "\n",
+    " * [Helm](https://github.com/kubernetes/helm)\n",
+    " * [Minikube](https://github.com/kubernetes/minikube)\n",
+    " * [S2I](https://github.com/openshift/source-to-image)\n",
+    " * R\n",
+    "\n",
+    "```bash\n",
+    "pip install sklearn\n",
+    "pip install grpcio-tools\n",
+    "```\n",
+    "\n",
+    "## Train locally\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm -f *ubyte && ./get_data.sh && Rscript train.R"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wrap model using s2i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!s2i build . seldonio/seldon-core-s2i-r r-mnist:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker run --name \"mnist_predictor\" -d --rm -p 5000:5000 r-mnist:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../../../wrappers/testing && make build_protos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Send some random features that conform to the contract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker rm mnist_predictor --force"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube start --memory 4096 --feature-gates=CustomResourceValidation=true --extra-config=apiserver.Authorization.Mode=RBAC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm install ../../../helm-charts/seldon-core-crd --name seldon-core-crd  --set usage_metrics.enabled=true\n",
+    "!helm install ../../../helm-charts/seldon-core --name seldon-core "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!eval $(minikube docker-env) && s2i build . seldonio/seldon-core-s2i-r r-mnist:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create -f r_mnist_deployment.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wait until ready (replicas == replicasAvailable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl get seldondeployments seldon-deployment-example -o jsonpath='{.status}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../../../util/api_tester && make build_protos "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../../util/api_tester/api-tester.py contract.json \\\n",
+    "    `minikube ip` `kubectl get svc -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].spec.ports[0].nodePort}'` \\\n",
+    "    --oauth-key oauth-key --oauth-secret oauth-secret -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube delete"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/models/sklearn_iris/README.md b/examples/models/sklearn_iris/README.md
deleted file mode 100644
index 4ca4dabc96..0000000000
--- a/examples/models/sklearn_iris/README.md
+++ /dev/null
@@ -1,91 +0,0 @@
-# SKLearn Iris
-A scikit-learn Iris model.
-
-## Depenencies
-
-```bash
-pip install sklearn
-pip install grpcio-tools
-```
-
-## Train locally
-
-```bash
-python train_iris.py
-```
-
-## Wrap using [s2i](https://github.com/openshift/source-to-image#installation).
-
-```bash
-s2i build . seldonio/seldon-core-s2i-python3 sklearn-iris:0.1
-```
-
-## Local Docker Smoke Test
-
-Run under docker.
-
-```bash
-docker run --rm -p 5000:5000 sklearn-iris:0.1
-```
-
-Ensure test grpc modules compiled.
-
-```bash
-pushd ../../../wrappers/testing ; make build_protos ; popd
-```
-
-Send a data request using the wrapper tester.
-
-```bash
-python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p
-```
-
-## Minikube test
-
-```bash
-minikube start --memory 4096
-```
-
-Install seldon core
-
-```
-helm install seldon-core-crd --name seldon-core-crd --repo https://storage.googleapis.com/seldon-charts --set usage_metrics.enabled=true --set rbac.enabled=false
-helm install seldon-core --name seldon-core --repo https://storage.googleapis.com/seldon-charts --set rbac.enabled=false
-```
-
-Connect to Minikube Docker daemon
-
-```bash
-eval $(minikube docker-env)
-```
-
-Build image using minikube docker daemon.
-
-```bash
-s2i build . seldonio/seldon-core-s2i-python3 sklearn-iris:0.1
-```
-
-Launch deployment
-
-```bash
-kubectl create -f sklearn_iris_deployment.json
-```
-
-Port forward API server
-
-```bash
-kubectl port-forward $(kubectl get pods -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].metadata.name}') 8080:8080
-```
-
-Ensure tester gRPC modules compiled. 
-
-```bash
-pushd ../../../util/api_tester ; make build_protos ; popd
-```
-
-Send test request
-```bash
-python ../../../util/api_tester/api-tester.py contract.json 0.0.0.0 8080 --oauth-key oauth-key --oauth-secret oauth-secret -p
-```
-
-
diff --git a/examples/models/sklearn_iris/sklearn_iris.ipynb b/examples/models/sklearn_iris/sklearn_iris.ipynb
new file mode 100644
index 0000000000..15fdc51c43
--- /dev/null
+++ b/examples/models/sklearn_iris/sklearn_iris.ipynb
@@ -0,0 +1,241 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scikit-Learn IRIS Model\n",
+    "\n",
+    " * Wrap a scikit-learn python model for use as a prediction microservice in seldon-core\n",
+    "   * Run locally on Docker to test\n",
+    "   * Deploy on seldon-core running on minikube\n",
+    " \n",
+    "## Depenencies\n",
+    "\n",
+    " * [Helm](https://github.com/kubernetes/helm)\n",
+    " * [Minikube](https://github.com/kubernetes/minikube)\n",
+    " * [S2I](https://github.com/openshift/source-to-image)\n",
+    "\n",
+    "```bash\n",
+    "pip install sklearn\n",
+    "pip install grpcio-tools\n",
+    "```\n",
+    "\n",
+    "## Train locally\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import os\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.externals import joblib\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "def main():\n",
+    "    clf = LogisticRegression()\n",
+    "    p = Pipeline([('clf', clf)])\n",
+    "    print('Training model...')\n",
+    "    p.fit(X, y)\n",
+    "    print('Model trained!')\n",
+    "\n",
+    "    filename_p = 'IrisClassifier.sav'\n",
+    "    print('Saving model in %s' % filename_p)\n",
+    "    joblib.dump(p, filename_p)\n",
+    "    print('Model saved!')\n",
+    "    \n",
+    "if __name__ == \"__main__\":\n",
+    "    print('Loading iris data set...')\n",
+    "    iris = datasets.load_iris()\n",
+    "    X, y = iris.data, iris.target\n",
+    "    print('Dataset loaded!')\n",
+    "    main()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wrap model using s2i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!s2i build . seldonio/seldon-core-s2i-python3 sklearn-iris:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker run --name \"iris_predictor\" -d --rm -p 5000:5000 sklearn-iris:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../../../wrappers/testing && make build_protos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Send some random features that conform to the contract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker rm iris_predictor --force"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube start --memory 4096 --feature-gates=CustomResourceValidation=true --extra-config=apiserver.Authorization.Mode=RBAC\n",
+    "!kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm install ../../../helm-charts/seldon-core-crd --name seldon-core-crd  --set usage_metrics.enabled=true\n",
+    "!helm install ../../../helm-charts/seldon-core --name seldon-core "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!eval $(minikube docker-env) && s2i build . seldonio/seldon-core-s2i-python3 sklearn-iris:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create -f sklearn_iris_deployment.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wait until ready (replicas == replicasAvailable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl get seldondeployments seldon-deployment-example -o jsonpath='{.status}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../../../util/api_tester && make build_protos "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python ../../../util/api_tester/api-tester.py contract.json \\\n",
+    "    `minikube ip` `kubectl get svc -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].spec.ports[0].nodePort}'` \\\n",
+    "    --oauth-key oauth-key --oauth-secret oauth-secret -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube delete"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/ksonnet_ambassador_minikube.ipynb b/notebooks/ksonnet_ambassador_minikube.ipynb
index 677c067985..36ea4a232c 100644
--- a/notebooks/ksonnet_ambassador_minikube.ipynb
+++ b/notebooks/ksonnet_ambassador_minikube.ipynb
@@ -29,13 +29,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "!minikube start --memory=5000 --feature-gates=CustomResourceValidation=true"
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting local Kubernetes v1.9.4 cluster...\n",
+      "Starting VM...\n",
+      "Getting VM IP address...\n",
+      "Moving files into cluster...\n",
+      "Setting up certs...\n",
+      "Connecting to cluster...\n",
+      "Setting up kubeconfig...\n",
+      "Starting cluster components...\n",
+      "Kubectl is now configured to use the cluster.\n",
+      "Loading cached images from config file.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!minikube start --memory 4096 --feature-gates=CustomResourceValidation=true --extra-config=apiserver.Authorization.Mode=AlwaysAllow"
    ]
   },
   {
@@ -47,11 +62,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "namespace \"seldon\" created\r\n"
+     ]
+    }
+   ],
    "source": [
     "!kubectl create namespace seldon"
    ]
@@ -66,22 +87,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "service \"ambassador\" created\r\n"
+     ]
+    }
+   ],
    "source": [
     "!kubectl apply -f resources/ambassador-service.yaml -n seldon"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "service \"ambassador-admin\" created\r\n",
+      "deployment \"ambassador\" created\r\n"
+     ]
+    }
+   ],
    "source": [
     "!kubectl apply -f https://getambassador.io/yaml/ambassador/ambassador-no-rbac.yaml -n seldon"
    ]
@@ -96,22 +130,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34mINFO  \u001b[0mUsing context 'minikube' from the kubeconfig file specified at the environment variable $KUBECONFIG\n",
+      "\u001b[34mINFO  \u001b[0mCreating environment \"default\" with namespace \"default\", pointing to cluster at address \"https://192.168.99.100:8443\"\n",
+      "\u001b[34mINFO  \u001b[0mGenerating ksonnet-lib data at path '/home/clive/work/seldon-core/fork-seldon-core/notebooks/my-ml-deployment/lib/v1.8.0'\n",
+      "\u001b[34mINFO  \u001b[0mksonnet app successfully created! Next, try creating a component with `ks generate`.\n"
+     ]
+    }
+   ],
    "source": [
     "!ks init my-ml-deployment --api-spec=version:v1.8.0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34mINFO  \u001b[0mRetrieved 12 files\n",
+      "\u001b[34mINFO  \u001b[0mWriting component at '/home/clive/work/seldon-core/fork-seldon-core/notebooks/my-ml-deployment/components/seldon-core.jsonnet'\n"
+     ]
+    }
+   ],
    "source": [
     "!cd my-ml-deployment && \\\n",
     "    ks registry add seldon-core github.com/SeldonIO/seldon-core/tree/master/seldon-core && \\\n",
@@ -121,11 +171,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34mINFO  \u001b[0mUpdating deployments seldon.seldon-cluster-manager\n",
+      "\u001b[34mINFO  \u001b[0mCreating non-existent deployments seldon.seldon-cluster-manager\n",
+      "\u001b[34mINFO  \u001b[0mUpdating deployments seldon.redis\n",
+      "\u001b[34mINFO  \u001b[0mCreating non-existent deployments seldon.redis\n",
+      "\u001b[34mINFO  \u001b[0mUpdating services seldon.redis\n",
+      "\u001b[34mINFO  \u001b[0mCreating non-existent services seldon.redis\n",
+      "\u001b[34mINFO  \u001b[0mUpdating customresourcedefinitions seldondeployments.machinelearning.seldon.io\n",
+      "\u001b[34mINFO  \u001b[0mCreating non-existent customresourcedefinitions seldondeployments.machinelearning.seldon.io\n"
+     ]
+    }
+   ],
    "source": [
     "!cd my-ml-deployment && \\\n",
     "      ks apply default"
@@ -147,10 +210,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "!cp ../proto/prediction.proto ./proto\n",
@@ -166,10 +227,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import requests\n",
@@ -474,21 +533,21 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/kubectl_demo_minikube.ipynb b/notebooks/kubectl_demo_minikube.ipynb
index 0c8a01633d..899c1690bf 100644
--- a/notebooks/kubectl_demo_minikube.ipynb
+++ b/notebooks/kubectl_demo_minikube.ipynb
@@ -34,7 +34,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!minikube start --memory=5000 --feature-gates=CustomResourceValidation=true"
+    "!minikube start --memory=5000 --feature-gates=CustomResourceValidation=true --extra-config=apiserver.Authorization.Mode=AlwaysAllow"
    ]
   },
   {
diff --git a/notebooks/kubectl_demo_minikube_rbac.ipynb b/notebooks/kubectl_demo_minikube_rbac.ipynb
index 744c42dfc5..40bd736fbc 100644
--- a/notebooks/kubectl_demo_minikube_rbac.ipynb
+++ b/notebooks/kubectl_demo_minikube_rbac.ipynb
@@ -34,7 +34,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!minikube start --memory=5000 --feature-gates=CustomResourceValidation=true --extra-config=apiserver.Authorization.Mode=RBAC\n",
+    "!minikube start --memory=5000 --feature-gates=CustomResourceValidation=true --extra-config=apiserver.Authorization.Mode=RBAC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "!kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default"
    ]
   },
diff --git a/readme.md b/readme.md
index aaad9a07ed..37df51b383 100644
--- a/readme.md
+++ b/readme.md
@@ -48,8 +48,7 @@ Machine learning deployment has many [challenges](./docs/challenges.md). Seldon
  - [Quick Start tutorial using Minikube](./docs/getting_started/minikube.md)
  - Jupyter notebooks showing worked examples:
     * Minikube:
-         *  [Jupyter Notebook showing deployment of prebuilt model using Minikube - with RBAC](https://github.com/SeldonIO/seldon-core/blob/master/notebooks/kubectl_demo_minikube_rbac.ipynb)
-         *  [Jupyter Notebook showing deployment of prebuilt model using Minikube - no RBAC](https://github.com/SeldonIO/seldon-core/blob/master/notebooks/kubectl_demo_minikube.ipynb)
+         * [Jupyter Notebook showing deployment of prebuilt model using Minikube - with RBAC](https://github.com/SeldonIO/seldon-core/blob/master/notebooks/kubectl_demo_minikube_rbac.ipynb)
          * [Jupyter notebook to create seldon-core with ksonnet and expose APIs using Ambassador on Minikube.](https://github.com/SeldonIO/seldon-core/blob/master/notebooks/ksonnet_ambassador_minikube.ipynb)
     * GCP:
          * [Jupyter Notebook showing deployment of prebuilt model using GCP cluster](https://github.com/SeldonIO/seldon-core/blob/master/notebooks/kubectl_demo_gcp.ipynb)
@@ -65,9 +64,15 @@ Machine learning deployment has many [challenges](./docs/challenges.md). Seldon
 Seldon-core allows various types of components to be built and plugged into the runtime prediction graph. These include [models, routers, transformers and combiners](docs/reference/internal-api.md). Some example components that are available as part of the project are:
 
  * **Models** : example that illustrate simple machine learning models to help you build your own integrations
-   * [Tensorflow MNIST Classifier](./examples/models/deep_mnist/README.md)
-   * [Keras MNIST Classifier](./examples/models/keras_mnist/README.md)
-   * [scikit-learn Iris Classifier](./examples/models/sklearn_iris/README.md)
+   * Python
+      * [Tensorflow MNIST Classifier](./examples/models/deep_mnist/deep_mnist.ipynb)
+      * [Keras MNIST Classifier](./examples/models/keras_mnist/keras_mnist.ipynb)
+      * [Scikit-learn Iris Classifier](./examples/models/sklearn_iris/sklearn_iris.ipynb)
+   * R
+      * [R MNIST Classifier](./examples/models/r_mnist/r_mnist.ipynb)
+      * [R Iris Classifier](./examples/models/r_iris/r_iris.ipynb)
+   * Java
+      * [H2O Classifier](./examples/models/h2o_mojo/h2o_model.ipynb)
  * **routers**
    * [Epsilon-greedy multi-armed bandits for real time optimization of models](https://github.com/SeldonIO/seldon-core/blob/master/notebooks/epsilon_greedy_gcp.ipynb)
  * **transformers**