From b655845003e6f538f53f97f907ee48aad7bd453d Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Mon, 17 Jul 2023 16:54:37 -0400
Subject: [PATCH 01/13] abn updates

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 docs/tutorials/abn/abn.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/tutorials/abn/abn.md b/docs/tutorials/abn/abn.md
index c251ee98..48802f18 100644
--- a/docs/tutorials/abn/abn.md
+++ b/docs/tutorials/abn/abn.md
@@ -102,12 +102,13 @@ In separate shells, port-forward requests to the frontend component and generate
     ```
     ```shell
     curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.0/samples/abn-sample/generate_load.sh | sh -s --
+    # source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/abn-sample/generate_load.sh
     ```
     <!-- # source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/abn-sample/generate_load.sh -->
 
 ## Deploy a candidate version
 
-Deploy the candidate version of the *backend* component, naming it `backend-candidate-1`.
+Deploy a candidate version of the *backend* component, naming it *backend-candidate-1*.
 
 ```shell
 kubectl create deployment backend-candidate-1 --image=iter8/abn-sample-backend:0.13-v2

From d115d3eab076210631dc85b8b7e0e2fef7ed5325 Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Tue, 18 Jul 2023 16:26:50 -0400
Subject: [PATCH 02/13] reviwer comments, kustomize, python

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 docs/tutorials/abn/abn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorials/abn/abn.md b/docs/tutorials/abn/abn.md
index 48802f18..42062a4e 100644
--- a/docs/tutorials/abn/abn.md
+++ b/docs/tutorials/abn/abn.md
@@ -108,7 +108,7 @@ In separate shells, port-forward requests to the frontend component and generate
 
 ## Deploy a candidate version
 
-Deploy a candidate version of the *backend* component, naming it *backend-candidate-1*.
+Deploy the candidate version of the *backend* component, naming it `backend-candidate-1`.
 
 ```shell
 kubectl create deployment backend-candidate-1 --image=iter8/abn-sample-backend:0.13-v2

From be4a66301d788cc8dfc3c43d0cecfc02564bb869 Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Fri, 21 Jul 2023 11:33:21 -0400
Subject: [PATCH 03/13] bg and canary for kserve

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 .../integrations/kserve-mm/blue-green.md      |    6 +-
 .../integrations/kserve-mm/canary.md          |    7 +-
 .../integrations/kserve/blue-green.md         |  262 ++
 .../integrations/kserve/canary-testing.md     |    2 +-
 docs/tutorials/integrations/kserve/canary.md  |  252 ++
 .../integrations/kserve/images/blue-green.png |  Bin 0 -> 77059 bytes
 .../integrations/kserve/images/canary.png     |  Bin 0 -> 66733 bytes
 .../kserve/images/src/blue-green.excalidraw   | 2499 +++++++++++++++++
 mkdocs.yml                                    |    4 +-
 samples/kserve-serving/sleep.sh               |   62 +
 10 files changed, 3087 insertions(+), 7 deletions(-)
 create mode 100644 docs/tutorials/integrations/kserve/blue-green.md
 create mode 100644 docs/tutorials/integrations/kserve/canary.md
 create mode 100644 docs/tutorials/integrations/kserve/images/blue-green.png
 create mode 100644 docs/tutorials/integrations/kserve/images/canary.png
 create mode 100644 docs/tutorials/integrations/kserve/images/src/blue-green.excalidraw

diff --git a/docs/tutorials/integrations/kserve-mm/blue-green.md b/docs/tutorials/integrations/kserve-mm/blue-green.md
index aa563b1f..f92cc76e 100644
--- a/docs/tutorials/integrations/kserve-mm/blue-green.md
+++ b/docs/tutorials/integrations/kserve-mm/blue-green.md
@@ -123,7 +123,7 @@ To send inference requests to the model:
       localhost:8080 inference.GRPCInferenceService.ModelInfer
       ```
 
-Note that the model version responding to each inference request can be determined from the `modelName` field of the response.
+Note that the model version responding to each inference request is noted in the response header `mm-vmodel-id`. In the requests above, we display only the response code and this header.
 
 ## Deploy a candidate model
 
@@ -164,7 +164,7 @@ The deployment of the candidate model triggers an automatic reconfiguration by I
 kubectl get virtualservice wisdom -o yaml
 ```
 
-Send additional inference requests as described above.
+Further, you can send additional inference requests as described above. They will be handled by both versions of the model.
 
 ## Modify weights (optional)
 
@@ -219,6 +219,8 @@ EOF
 
 ### Delete the candidate `InferenceService`
 
+Once the primary `InferenceService` has been redeployed, delete the candidate:
+
 ```shell
 kubectl delete inferenceservice wisdom-1
 ```
diff --git a/docs/tutorials/integrations/kserve-mm/canary.md b/docs/tutorials/integrations/kserve-mm/canary.md
index 403ab10a..53f09b62 100644
--- a/docs/tutorials/integrations/kserve-mm/canary.md
+++ b/docs/tutorials/integrations/kserve-mm/canary.md
@@ -101,7 +101,6 @@ To send inference requests to the model:
     cd demo
     cat wisdom.sh
     . wisdom.sh
-    . wisdom-test.sh
     ```
     or, to send a request with header `traffic: test`:
     ```shell
@@ -137,7 +136,7 @@ To send inference requests to the model:
       localhost:8080 inference.GRPCInferenceService.ModelInfer
       ```
 
-Note that the model version responding to each inference request can be determined from the `modelName` field of the response.
+Note that the model version responding to each inference request is noted in the response header `mm-vmodel-id`. In the requests above, we display only the response code and this header.
 
 ## Deploy a candidate model
 
@@ -178,7 +177,7 @@ The deployment of the candidate model triggers an automatic reconfiguration by I
 kubectl get virtualservice wisdom -o yaml
 ```
 
-Send additional inference requests as described above.
+Further, you can send additional inference requests as described above. They will be handled by both versions of the model.
 
 ## Promote the candidate model
 
@@ -213,6 +212,8 @@ EOF
 
 ### Delete the candidate `InferenceService`
 
+Once the primary `InferenceService` has been redeployed, delete the candidate:
+
 ```shell
 kubectl delete inferenceservice wisdom-1
 ```
diff --git a/docs/tutorials/integrations/kserve/blue-green.md b/docs/tutorials/integrations/kserve/blue-green.md
new file mode 100644
index 00000000..d3ffed1e
--- /dev/null
+++ b/docs/tutorials/integrations/kserve/blue-green.md
@@ -0,0 +1,262 @@
+---
+template: main.html
+---
+
+# Blue-Green Rollout of a KServe ML Model
+
+This tutorial shows how Iter8 can be used to implement a blue-green rollout of ML models hosted in a KServe environment. In a blue-green rollout, a percentage of inference requests are directed to a candidate version of the model. The remaining requests go to the primary, or initial, version of the model. Iter8 enables a blue-green rollout by automatically configuring routing resources to distribute inference requests.
+
+After a one time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Optionally, the end user can modify the percentage of inference requests being sent to the candidate model. Iter8 automatically handles all underlying network configuration.
+
+![Blue-Green rollout](images/blue-green.png)
+
+???+ "Before you begin"
+    1. Ensure that you have the [kubectl CLI](https://kubernetes.io/docs/reference/kubectl/).
+    2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.10/get_started/#before-you-begin) environment as follows:
+    ```shell
+    curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.10/hack/quick_install.sh" | bash
+    ```
+<!-- Istio 1.15 already installed 
+    3. Install [Istio](https://istio.io). You can install the [demo profile](https://istio.io/latest/docs/setup/getting-started/).
+    ```shell
+    istioctl install --set profile=demo -y
+    ```
+-->
+
+## Install the Iter8 controller
+
+--8<-- "docs/tutorials/installiter8controller.md"
+
+## Deploy a primary model
+
+Deploy the primary version of a model using an `InferenceService`:
+
+```shell
+cat <<EOF | kubectl apply -f -
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: wisdom-0
+  labels:
+    app.kubernetes.io/name: wisdom
+    app.kubernetes.io/version: v0
+    iter8.tools/watch: "true"
+spec:
+  predictor:
+    minReplicas: 1
+    model:
+      modelFormat:
+        name: sklearn
+      runtime: kserve-mlserver
+      storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+```
+
+??? note "About the primary `InferenceService`"
+    Naming the model with the suffix `-0` (and the candidate with the suffix `-1`) simplifies the rollout initialization. However, any name can be specified.
+    
+    The label `iter8.tools/watch: "true"` lets Iter8 know that it should pay attention to changes to this `InferenceService`.
+
+Inspect the deployed `InferenceService`:
+
+```shell
+kubectl get inferenceservice wisdom-0
+```
+
+When the `READY` field becomes `True`, the model is fully deployed.
+    
+## Initialize the Blue-Green routing policy
+
+Initialize model rollout with a blue-green traffic pattern as follows:
+
+```shell
+#cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl apply -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/traffic-templates -f - | kubectl apply -f -
+templateName: initialize-rollout
+targetEnv: kserve
+trafficStrategy: blue-green
+modelName: wisdom
+EOF
+```
+
+The `initialize-rollout` template (with `trafficStrategy: blue-green`) configures the Istio service mesh to route all requests to the primary version of the model (`wisdom-0`). Further, it defines the routing policy that will be used by Iter8 when it observes changes in the models. By default, this routing policy splits inference requests 50-50 between the primary and candidate versions. For detailed configuration options, see the Helm chart.
+
+## Verify network configuration
+
+To verify the network configuration, you can inspect the network configuration:
+
+```shell
+kubectl get virtualservice -o yaml wisdom
+```
+
+To send inference requests to the model:
+
+=== "From within the cluster"
+    1. Create a "sleep" pod in the cluster from which requests can be made:
+    ```shell
+    # curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.0/samples/kserve-serving/sleep.sh | sh -
+    source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/kserve-serving/sleep.sh
+    ```
+
+    2. exec into the sleep pod:
+    ```shell
+    kubectl exec --stdin --tty "$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)" -c sleep -- /bin/sh
+    ```
+
+    3. Make inference requests:
+    ```shell
+    cat wisdom.sh
+    . wisdom.sh
+    ```
+
+=== "From outside the cluster"
+    1. In a separate terminal, port-forward the ingress gateway:
+      ```shell
+      kubectl -n istio-system port-forward svc/knative-local-gateway 8080:80
+      ```
+
+    2. Download the sample input:
+      ```shell
+      # curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.0/samples/kserve-serving/input.json
+      ```
+
+    3. Send inference requests:
+      ```shell
+      curl -H 'Content-Type: application/json' -H 'Host: wisdom.default' localhost:8080 -d @input.json -s -D - \
+      | grep -e HTTP -e mm-vmodel-id
+      ```
+
+Note that the model version responding to each inference request is noted in the response header `mm-vmodel-id`. In the requests above, we display only the response code and this header.
+
+## Deploy a candidate model
+
+Deploy a candidate model using a second `InferenceService`:
+
+```shell
+cat <<EOF | kubectl apply -f -
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: wisdom-1
+  labels:
+    app.kubernetes.io/name: wisdom
+    app.kubernetes.io/version: v1
+    iter8.tools/watch: "true"
+spec:
+  predictor:
+    minReplicas: 1
+    model:
+      modelFormat:
+        name: sklearn
+      runtime: kserve-mlserver
+      storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+```
+
+??? note "About the candidate `InferenceService`"
+    The model name (`wisdom`) and version (`v1`) are recorded using the labels `app.kubernets.io/name` and `app.kubernets.io.version`.
+
+    In this tutorial, the model source (field `spec.predictor.model.storageUri`) is the same as for the primary version of the model. In a real world example, this would be different.
+
+## Verify network configuration changes
+
+The deployment of the candidate model triggers an automatic reconfiguration by Iter8. Inspect the `VirtualService` to see that inference requests are now distributed between the primary model and the secondary model:
+
+```shell
+kubectl get virtualservice wisdom -o yaml
+```
+
+Further, you can send additional inference requests as described above. They will be handled by both versions of the model.
+
+## Modify weights (optional)
+
+You can modify the weight distribution of inference requests using the Iter8 `traffic-template` chart:
+
+```shell
+#cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl apply -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/traffic-templates -f - | kubectl apply -f -
+templateName: modify-weights
+targetEnv: kserve
+trafficStrategy: blue-green
+modelName: wisdom
+modelVersions:
+  - weight: 20
+  - weight: 80
+EOF
+```
+
+Note that using the `modify-weights` overrides the default traffic split for all future candidate deployments.
+
+As above, you can verify the network configuration changes.
+
+## Promote the candidate model
+
+Promoting the candidate involves redefining the primary `InferenceService` using the new model and deleting the candidate `InferenceService`.
+
+### Redefine the primary `InferenceService`
+
+```shell
+cat <<EOF | kubectl replace -f -
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: wisdom-0
+  labels:
+    app.kubernetes.io/name: wisdom
+    app.kubernetes.io/version: v1
+    iter8.tools/watch: "true"
+spec:
+  predictor:
+    minReplicas: 1
+    model:
+      modelFormat:
+        name: sklearn
+      runtime: kserve-mlserver
+      storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+```
+
+??? note "What is different?"
+    The version label (`app.kubernets.io/version`) was updated. In a real world example, `spec.predictor.model.storageUri` would also be updated.
+
+### Delete the candidate `InferenceService`
+
+Once the primary `InferenceService` has been redeployed, delete the candidate:
+
+```shell
+kubectl delete inferenceservice wisdom-1
+```
+
+### Verify network configuration changes
+
+Inspect the `VirtualService` to see that the it has been automatically reconfigured to send requests only to the primary model.
+
+## Clean up
+
+Delete the candidate model:
+
+```shell
+kubectl delete --force isvc/wisdom-1
+```
+
+Delete routing artifacts:
+
+```shell
+#cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl delete --force -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/traffic-templates -f - | kubectl apply -f -
+templateName: initialize-rollout
+targetEnv: kserve
+trafficStrategy: blue-green
+modelName: wisdom
+EOF
+```
+
+Delete the primary model:
+
+```shell
+kubectl delete --force isvc/wisdom-0
+```
+
+Uninstall the Iter8 controller:
+
+--8<-- "docs/tutorials/deleteiter8controller.md"
diff --git a/docs/tutorials/integrations/kserve/canary-testing.md b/docs/tutorials/integrations/kserve/canary-testing.md
index 862e64c2..8ed630c9 100644
--- a/docs/tutorials/integrations/kserve/canary-testing.md
+++ b/docs/tutorials/integrations/kserve/canary-testing.md
@@ -2,7 +2,7 @@
 template: main.html
 ---
 
-# Canary Testing
+# Load Testing Multiple Model Versions
 
 This tutorial shows how easy it is validate SLOs for multiple versions of a model in [KServe](https://kserve.github.io/website/0.10/) when fetching metrics from a metrics database like Prometheus. We show this using the `sklearn-iris` model used to describe [canary rollouts](https://kserve.github.io/website/0.10/modelserving/v1beta1/rollout/canary-example/) in KServe. 
 
diff --git a/docs/tutorials/integrations/kserve/canary.md b/docs/tutorials/integrations/kserve/canary.md
new file mode 100644
index 00000000..5a2924bd
--- /dev/null
+++ b/docs/tutorials/integrations/kserve/canary.md
@@ -0,0 +1,252 @@
+---
+template: main.html
+---
+
+# Canary Rollout of a KServe ML Model
+
+This tutorial shows how Iter8 can be used to implement a canary rollout of ML models hosted in a KServe environment. In a canary rollout, inference requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. Iter8 enables a canary rollout by automatically configuring the network to distribute inference requests.
+
+After a one time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Iter8 automatically handles the underlying network configuration.
+
+![Canary rollout](images/canary.png)
+
+???+ "Before you begin"
+    1. Ensure that you have the [kubectl CLI](https://kubernetes.io/docs/reference/kubectl/).
+    2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.10/get_started/#before-you-begin) environment as follows:
+    ```shell
+    curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.10/hack/quick_install.sh" | bash
+    ```
+<!-- Istio 1.15 already installed 
+    3. Install [Istio](https://istio.io). You can install the [demo profile](https://istio.io/latest/docs/setup/getting-started/).
+    ```shell
+    istioctl install --set profile=demo -y
+    ```
+-->
+
+## Install the Iter8 controller
+
+--8<-- "docs/tutorials/installiter8controller.md"
+
+## Deploy a primary model
+
+Deploy the primary version of a model using an `InferenceService`:
+
+```shell
+cat <<EOF | kubectl apply -f -
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: wisdom-0
+  labels:
+    app.kubernetes.io/name: wisdom
+    app.kubernetes.io/version: v0
+    iter8.tools/watch: "true"
+spec:
+  predictor:
+    minReplicas: 1
+    model:
+      modelFormat:
+        name: sklearn
+      runtime: kserve-mlserver
+      storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+```
+
+??? note "About the primary `InferenceService`"
+    Naming the model with the suffix `-0` (and the candidate with the suffix `-1`) simplifies the rollout initialization. However, any name can be specified.
+    
+    The label `iter8.tools/watch: "true"` lets Iter8 know that it should pay attention to changes to this `InferenceService`.
+
+Inspect the deployed `InferenceService`:
+
+```shell
+kubectl get inferenceservice wisdom-0
+```
+
+When the `READY` field becomes `True`, the model is fully deployed.
+    
+## Initialize the Canary routing policy
+
+Initialize model rollout with a canary traffic pattern as follows:
+
+```shell
+#cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl apply -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/traffic-templates -f - | kubectl apply -f -
+templateName: initialize-rollout
+targetEnv: kserve
+trafficStrategy: canary
+modelName: wisdom
+EOF
+```
+
+The `initialize-rollout` template (with `trafficStrategy: canary`) configures the service mesh to route all requests to the primary version of the model (`wisdom-0`). Further, it defines the routing policy that will be used by Iter8 when it observes changes in the models. By default, this routing policy sends inference requests with the header `traffic` set to the value `test` to the candidate version of the model and all remaining inference requests to the primary version of the model. For detailed configuration options, see the Helm chart.
+
+## Verify network configuration
+
+To verify the network configuration, you can inspect the network configuration:
+
+```shell
+kubectl get virtualservice -o yaml wisdom
+```
+
+To send inference requests to the model:
+
+=== "From within the cluster"
+    1. Create a "sleep" pod in the cluster from which requests can be made:
+    ```shell
+    # curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.0/samples/kserve-serving/sleep.sh | sh -
+    source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/kserve-serving/sleep.sh
+    ```
+
+    2. exec into the sleep pod:
+    ```shell
+    kubectl exec --stdin --tty "$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)" -c sleep -- /bin/sh
+    ```
+
+    3. Make inference requests:
+    ```shell
+    cat wisdom.sh
+    . wisdom.sh
+    ```
+    or, to send a request with header `traffic: test`:
+    ```shell
+    cat wisdom-test.sh
+    . wisdom-test.sh
+    ```
+
+=== "From outside the cluster"
+    1. In a separate terminal, port-forward the ingress gateway:
+      ```shell
+      kubectl -n istio-system port-forward svc/knative-local-gateway 8080:80
+      ```
+
+    2. Download the sample input:
+      ```shell
+      # curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.0/samples/kserve-serving/input.json
+      ```
+
+    3. Send inference requests:
+      ```shell
+      curl -H 'Content-Type: application/json' -H 'Host: wisdom.default' localhost:8080 -d @input.json -s -D - \
+      | grep -e HTTP -e mm-vmodel-id
+      ```
+    or, to send a request with header `traffic: test`:
+    ```shell
+      curl -H 'Content-Type: application/json' -H 'Host: wisdom.default' localhost:8080 -d @input.json -s -D - \
+      -H 'traffic: test' \
+      | grep -e HTTP -e mm-vmodel-id
+    ```
+
+Note that the model version responding to each inference request is noted in the response header `mm-vmodel-id`. In the requests above, we display only the response code and this header.
+
+## Deploy a candidate model
+
+Deploy a candidate model using a second `InferenceService`:
+
+```shell
+cat <<EOF | kubectl apply -f -
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: wisdom-1
+  labels:
+    app.kubernetes.io/name: wisdom
+    app.kubernetes.io/version: v1
+    iter8.tools/watch: "true"
+spec:
+  predictor:
+    minReplicas: 1
+    model:
+      modelFormat:
+        name: sklearn
+      runtime: kserve-mlserver
+      storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+```
+
+??? note "About the candidate `InferenceService`"
+    The model name (`wisdom`) and version (`v1`) are recorded using the labels `app.kubernets.io/name` and `app.kubernets.io.version`.
+
+    In this tutorial, the model source (field `spec.predictor.model.storageUri`) is the same as for the primary version of the model. In a real world example, this would be different.
+
+## Verify network configuration changes
+
+The deployment of the candidate model triggers an automatic reconfiguration by Iter8. Inspect the `VirtualService` to see that inference requests are now distributed between the primary model and the secondary model:
+
+```shell
+kubectl get virtualservice wisdom -o yaml
+```
+
+Further, you can send additional inference requests as described above. They will be handled by both versions of the model.
+
+## Promote the candidate model
+
+Promoting the candidate involves redefining the primary `InferenceService` using the new model and deleting the candidate `InferenceService`.
+
+### Redefine the primary `InferenceService`
+
+```shell
+cat <<EOF | kubectl replace -f -
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: wisdom-0
+  labels:
+    app.kubernetes.io/name: wisdom
+    app.kubernetes.io/version: v1
+    iter8.tools/watch: "true"
+spec:
+  predictor:
+    minReplicas: 1
+    model:
+      modelFormat:
+        name: sklearn
+      runtime: kserve-mlserver
+      storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+```
+
+??? note "What is different?"
+    The version label (`app.kubernets.io/version`) was updated. In a real world example, `spec.predictor.model.storageUri` would also be updated.
+
+### Delete the candidate `InferenceService`
+
+Once the primary `InferenceService` has been redeployed, delete the candidate:
+
+```shell
+kubectl delete inferenceservice wisdom-1
+```
+
+### Verify network configuration changes
+
+Inspect the `VirtualService` to see that the it has been automatically reconfigured to send requests only to the primary model.
+
+## Clean up
+
+Delete the candidate model:
+
+```shell
+kubectl delete --force isvc/wisdom-1
+```
+
+Delete routing artifacts:
+
+```shell
+#cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl delete --force -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/traffic-templates -f - | kubectl delete -f -
+templateName: initialize-rollout
+targetEnv: kserve
+trafficStrategy: canary
+modelName: wisdom
+EOF
+```
+
+Delete the primary model:
+
+```shell
+kubectl delete --force isvc/wisdom-0
+```
+
+Uninstall the Iter8 controller:
+
+--8<-- "docs/tutorials/deleteiter8controller.md"
diff --git a/docs/tutorials/integrations/kserve/images/blue-green.png b/docs/tutorials/integrations/kserve/images/blue-green.png
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new file mode 100644
index 00000000..51c53d16
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+      },
+      "boundElements": [],
+      "updated": 1683050458034,
+      "link": null,
+      "locked": false,
+      "startBinding": null,
+      "endBinding": null,
+      "lastCommittedPoint": null,
+      "startArrowhead": null,
+      "endArrowhead": null,
+      "points": [
+        [
+          0,
+          0
+        ],
+        [
+          -1.32379150390625,
+          216.01832580566406
+        ]
+      ]
+    },
+    {
+      "type": "arrow",
+      "version": 768,
+      "versionNonce": 375076641,
+      "isDeleted": false,
+      "id": "i9Mm5yulG1AKlZWRUnwJz",
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+      "strokeStyle": "dashed",
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+      "opacity": 100,
+      "angle": 0,
+      "x": 968.2029581053202,
+      "y": 194.457657735386,
+      "strokeColor": "#5c940d",
+      "backgroundColor": "transparent",
+      "width": 261.91211344690487,
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+      "seed": 3444577,
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+        "elementId": "LRk10fUvnXiCbXOksHEkb",
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+      "endArrowhead": "arrow",
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+        [
+          0,
+          0
+        ],
+        [
+          125.80560302734375,
+          -34.090179443359375
+        ],
+        [
+          261.91211344690487,
+          -2.263140178549662
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+      ]
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+    {
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+      "endArrowhead": "arrow",
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+        [
+          127.96251292341049,
+          -34.71360144700003
+        ],
+        [
+          264.48872752301986,
+          -3.0078214177031555
+        ]
+      ]
+    },
+    {
+      "type": "rectangle",
+      "version": 1386,
+      "versionNonce": 1108721395,
+      "isDeleted": false,
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+      "opacity": 100,
+      "angle": 0,
+      "x": 1143.502496879947,
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+      "strokeColor": "#ffffff",
+      "backgroundColor": "#c92a2a",
+      "width": 336,
+      "height": 32,
+      "seed": 1075500531,
+      "groupIds": [],
+      "roundness": {
+        "type": 1
+      },
+      "boundElements": [
+        {
+          "type": "text",
+          "id": "OjzI_cEFKnQZivd7KBLWr"
+        }
+      ],
+      "updated": 1683055428247,
+      "link": null,
+      "locked": false
+    },
+    {
+      "type": "text",
+      "version": 1719,
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+      "id": "OjzI_cEFKnQZivd7KBLWr",
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+      "strokeWidth": 1,
+      "strokeStyle": "solid",
+      "roughness": 1,
+      "opacity": 100,
+      "angle": 0,
+      "x": 1152.4225866016268,
+      "y": 462.7124423718664,
+      "strokeColor": "#ffffff",
+      "backgroundColor": "transparent",
+      "width": 318.1598205566406,
+      "height": 20,
+      "seed": 2143215997,
+      "groupIds": [],
+      "roundness": null,
+      "boundElements": [],
+      "updated": 1686590568022,
+      "link": null,
+      "locked": false,
+      "fontSize": 16,
+      "fontFamily": 1,
+      "text": "routing resources (automated by Iter8)",
+      "textAlign": "center",
+      "verticalAlign": "middle",
+      "containerId": "GLJMKNx_4-ss6shQ9v26u",
+      "originalText": "routing resources (automated by Iter8)",
+      "lineHeight": 1.25,
+      "baseline": 14
+    },
+    {
+      "type": "rectangle",
+      "version": 1123,
+      "versionNonce": 1353199507,
+      "isDeleted": false,
+      "id": "7A2q5t6APnHba2J9w99Xm",
+      "fillStyle": "solid",
+      "strokeWidth": 1,
+      "strokeStyle": "dotted",
+      "roughness": 1,
+      "opacity": 100,
+      "angle": 0,
+      "x": 608.6514836963534,
+      "y": 157.6252536511633,
+      "strokeColor": "#5c940d",
+      "backgroundColor": "#82c91e",
+      "width": 245,
+      "height": 30,
+      "seed": 1075500531,
+      "groupIds": [],
+      "roundness": {
+        "type": 1
+      },
+      "boundElements": [
+        {
+          "type": "text",
+          "id": "nRmlVNpVw3oHgWFLl0rnj"
+        }
+      ],
+      "updated": 1683055516130,
+      "link": null,
+      "locked": false
+    },
+    {
+      "type": "text",
+      "version": 1369,
+      "versionNonce": 230932673,
+      "isDeleted": false,
+      "id": "nRmlVNpVw3oHgWFLl0rnj",
+      "fillStyle": "hachure",
+      "strokeWidth": 1,
+      "strokeStyle": "solid",
+      "roughness": 1,
+      "opacity": 100,
+      "angle": 0,
+      "x": 618.8875524219393,
+      "y": 162.6252536511633,
+      "strokeColor": "#000000",
+      "backgroundColor": "transparent",
+      "width": 224.52786254882812,
+      "height": 20,
+      "seed": 2143215997,
+      "groupIds": [],
+      "roundness": null,
+      "boundElements": [],
+      "updated": 1686590568022,
+      "link": null,
+      "locked": false,
+      "fontSize": 16,
+      "fontFamily": 1,
+      "text": "app resources (any CD tool)",
+      "textAlign": "center",
+      "verticalAlign": "middle",
+      "containerId": "7A2q5t6APnHba2J9w99Xm",
+      "originalText": "app resources (any CD tool)",
+      "lineHeight": 1.25,
+      "baseline": 14
+    }
+  ],
+  "appState": {
+    "gridSize": null,
+    "viewBackgroundColor": "#ffffff"
+  },
+  "files": {}
+}
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index 102c4ab1..0451dbfa 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -135,7 +135,9 @@ nav:
       - Performance testing:
         - HTTP: tutorials/integrations/kserve/http.md
         - gRPC: tutorials/integrations/kserve/grpc.md
-      - Canary testing: tutorials/integrations/kserve/canary-testing.md
+        - Multiple Versions:  tutorials/integrations/kserve/canary-testing.md
+      - Blue-Green testing: tutorials/integrations/kserve/blue-green.md
+      - Canary testing: tutorials/integrations/kserve/canary.md
       - Continuous validation: tutorials/integrations/kserve/continuous-validation.md
       - Other (Iter8 v0.7): tutorials/integrations/kserve/kserve.md
     - Kserve (modelmesh):
diff --git a/samples/kserve-serving/sleep.sh b/samples/kserve-serving/sleep.sh
index e69de29b..263f4cb6 100644
--- a/samples/kserve-serving/sleep.sh
+++ b/samples/kserve-serving/sleep.sh
@@ -0,0 +1,62 @@
+#!/bin/sh
+cat <<EOF | kubectl apply -f -
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: sleep
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: sleep
+  template:
+    metadata:
+      labels:
+        app: sleep
+        sidecar.istio.io/inject: "true"
+    spec:
+      containers:
+      - name: sleep
+        image: curlimages/curl
+        command: ["/bin/sh", "-c", "sleep 3650d"]
+        workingDir: /demo
+        imagePullPolicy: IfNotPresent
+        volumeMounts:
+        - name: config-volume
+          mountPath: /demo
+        securityContext:
+          runAsNonRoot: true
+          runAsUser: 1001040000
+          allowPrivilegeEscalation: false
+      volumes:
+      - name: config-volume
+        configMap:
+          name: demo-input
+---
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: demo-input
+data:
+  input.json: |
+    {
+      "inputs": [
+        {
+          "name": "input-0",
+          "shape": [2, 4],
+          "datatype": "FP32",
+          "data": [
+            [6.8, 2.8, 4.8, 1.4],
+            [6.0, 3.4, 4.5, 1.6]
+          ]
+        }
+      ]
+    }
+  wisdom.sh: |
+    curl -H 'Content-Type: application/json' http://wisdom.default -d @input.json -s -D - \
+    | grep -e HTTP -e mm-vmodel-id
+  wisdom-test.sh: |
+    curl -H 'Content-Type: application/json' http://wisdom.default -d @input.json -s -D - \
+    -H 'traffic: test' \
+    | grep -e HTTP -e mm-vmodel-id
+EOF

From f422d95a73f0f29992be3640a07fdbee5c5e8b33 Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Tue, 25 Jul 2023 16:38:07 -0400
Subject: [PATCH 04/13] wording changes

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 .github/wordlist.txt                          |  6 +++++
 docs/tutorials/abn/abn.md                     | 22 ++++++++-----------
 .../integrations/kserve/blue-green.md         |  4 ++--
 docs/tutorials/integrations/kserve/canary.md  |  2 +-
 docs/user-guide/topics/ab_testing.md          |  2 +-
 5 files changed, 19 insertions(+), 17 deletions(-)

diff --git a/.github/wordlist.txt b/.github/wordlist.txt
index a6627faf..96bd8226 100644
--- a/.github/wordlist.txt
+++ b/.github/wordlist.txt
@@ -168,3 +168,9 @@ gz
 xvf
 IMG
 mv
+modelName
+src
+targetEnv
+templateName
+trafficStrategy
+modelVersions
\ No newline at end of file
diff --git a/docs/tutorials/abn/abn.md b/docs/tutorials/abn/abn.md
index 42062a4e..1b4af2fc 100644
--- a/docs/tutorials/abn/abn.md
+++ b/docs/tutorials/abn/abn.md
@@ -92,11 +92,11 @@ data:
 EOF
 ```
 
-In this definition, each version of the application is composed of a `Service` and a `Deployment`. In the primary version, both are named `backend`. In any candidate version they are named `backend-candidate-1`. Iter8 uses this definition to identify when any of the versions of the application are available. It can then respond appropriate to `Lookup()` requests. 
+In this definition, each version of the application is composed of a `Service` and a `Deployment`. In the primary version, both are named `backend`. In any candidate version they are named `backend-candidate-1`. Iter8 uses this definition to identify when any of the versions of the application are available. It can then respond appropriately to `Lookup()` requests. 
 
 ## Generate load
 
-In separate shells, port-forward requests to the frontend component and generate load for multiple users. A [script](https://raw.githubusercontent.com/iter8-tools/docs/main/samples/abn-sample/generate_load.sh) is provided to do this. To use it:
+In separate shells, port-forward requests to the frontend component and generate load for multiple users. A [script](https://raw.githubusercontent.com/iter8-tools/docs/main/samples/abn-sample/generate_load.sh) is provided for the latter.
     ```shell
     kubectl port-forward service/frontend 8090:8090
     ```
@@ -117,8 +117,8 @@ kubectl label deployment backend-candidate-1 iter8.tools/watch="true"
 kubectl expose deployment backend-candidate-1 --name=backend-candidate-1 --port=8091
 ```
 
-Until the candidate version is ready; that is, until all expected resources are deployed and available, calls to `Lookup()` will return only the index 0; the existing version.
-Once the candidate version is ready, `Lookup()` will return both indices (0 and 1) so that requests can be distributed across versions.
+Until the candidate version is ready; that is, until all expected resources are deployed and available, calls to `Lookup()` will return only the version number `0`; the existing version.
+Once the candidate version is ready, `Lookup()` will return both version numbers (`0` and `1`) so that requests can be distributed across versions.
 
 ## Compare versions using Grafana
 
@@ -128,24 +128,20 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana in a browser:
-
-```shell
-http://localhost:3000/
-```
+Open Grafana in a browser by going to http://localhost:3000/
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `Iter8` with:
 
-- URL `http://iter8.default:8080/metrics` and 
-- query string `application=default%2Fbackend`
+- URL: `http://iter8.default:8080/metrics`
+- Query string: `application=default%2Fbackend`
 
-[Create a new dashboard](http://localhost:3000/dashboards) by *import*. Do so by pasting the contents of this [JSON definition](https://gist.githubusercontent.com/Alan-Cha/aa4ba259cc4631aafe9b43500502c60f/raw/034249f24e2c524ee4e326e860c06149ae7b2677/gistfile1.txt) into the box and *load* it. Associate it with the JSON API data source defined above.
+[Create a new dashboard](http://localhost:3000/dashboards) by *import*. Copy and paste the contents of this [JSON definition](https://gist.githubusercontent.com/Alan-Cha/aa4ba259cc4631aafe9b43500502c60f/raw/034249f24e2c524ee4e326e860c06149ae7b2677/gistfile1.txt) into the text box and *load* it. Associate it with the JSON API data source above.
 
 The Iter8 dashboard allows you to compare the behavior of the two versions of the backend component against each other and select a winner. Since user requests are being sent by the load generation script, the values in the report may change over time.  The Iter8 dashboard may look like the following:
 
 ![A/B dashboard](images/dashboard.png)
 
-Once a winner is identified, the winner can be promoted, and the candidate version deleted.
+Once you identify a winner, it can be promoted, and the candidate version deleted.
 
 ## Promote candidate version
 
diff --git a/docs/tutorials/integrations/kserve/blue-green.md b/docs/tutorials/integrations/kserve/blue-green.md
index d3ffed1e..2fcba181 100644
--- a/docs/tutorials/integrations/kserve/blue-green.md
+++ b/docs/tutorials/integrations/kserve/blue-green.md
@@ -6,7 +6,7 @@ template: main.html
 
 This tutorial shows how Iter8 can be used to implement a blue-green rollout of ML models hosted in a KServe environment. In a blue-green rollout, a percentage of inference requests are directed to a candidate version of the model. The remaining requests go to the primary, or initial, version of the model. Iter8 enables a blue-green rollout by automatically configuring routing resources to distribute inference requests.
 
-After a one time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Optionally, the end user can modify the percentage of inference requests being sent to the candidate model. Iter8 automatically handles all underlying network configuration.
+After a one-time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Optionally, the end user can modify the percentage of inference requests being sent to the candidate model. Iter8 automatically handles all underlying network configuration.
 
 ![Blue-Green rollout](images/blue-green.png)
 
@@ -79,7 +79,7 @@ modelName: wisdom
 EOF
 ```
 
-The `initialize-rollout` template (with `trafficStrategy: blue-green`) configures the Istio service mesh to route all requests to the primary version of the model (`wisdom-0`). Further, it defines the routing policy that will be used by Iter8 when it observes changes in the models. By default, this routing policy splits inference requests 50-50 between the primary and candidate versions. For detailed configuration options, see the Helm chart.
+The `initialize-rollout` template (with `trafficStrategy: blue-green`) configures the Istio service mesh to route all requests to the primary version of the model (`wisdom-0`). Further, it defines the routing policy that will be used by Iter8 when it observes changes in the models. By default, this routing policy splits inference requests 50-50 between the primary and candidate versions. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/traffic/values.yaml).
 
 ## Verify network configuration
 
diff --git a/docs/tutorials/integrations/kserve/canary.md b/docs/tutorials/integrations/kserve/canary.md
index 5a2924bd..e4e36a27 100644
--- a/docs/tutorials/integrations/kserve/canary.md
+++ b/docs/tutorials/integrations/kserve/canary.md
@@ -6,7 +6,7 @@ template: main.html
 
 This tutorial shows how Iter8 can be used to implement a canary rollout of ML models hosted in a KServe environment. In a canary rollout, inference requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. Iter8 enables a canary rollout by automatically configuring the network to distribute inference requests.
 
-After a one time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Iter8 automatically handles the underlying network configuration.
+After a one-time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Iter8 automatically handles the underlying network configuration.
 
 ![Canary rollout](images/canary.png)
 
diff --git a/docs/user-guide/topics/ab_testing.md b/docs/user-guide/topics/ab_testing.md
index 3df278d0..213cecf0 100644
--- a/docs/user-guide/topics/ab_testing.md
+++ b/docs/user-guide/topics/ab_testing.md
@@ -11,7 +11,7 @@ A/B/n testing relies on business metrics typically computed by a frontend, user-
 Metric values often depend on one or more interactions with backend (not user-facing) application components. To run an A/B/n test on a backend component, it is necessary to be able to associate a metric value (computed by the frontend component) to the version of the backend component that contributed to its computation. 
 The challenge is that the frontend component often does not know which version of the backend component processed a given request. To address this challenge, Iter8 introduces an A/B/n SDK. 
 
-The Iter8 SDK uses a fixed set of versions numbers 0, 1, ... as a way to refer to the current set of versions of a Kubernetes application or ML model. The version of the application associated with a given version number changes over time as new versions are developed, deployed for testing, and either promoted or deleted. Since the set of version numbers is fixed, they can be used to configure routing to the application.
+The Iter8 SDK uses a fixed set of versions numbers (`0`, `1`, etc.) as a way to refer to the current set of versions of a Kubernetes application or ML model. The version of the application associated with a given version number changes over time as new versions are developed, deployed for testing, and either promoted or deleted. Since the set of version numbers is fixed, they can be used to configure routing to the application.
 
 The Iter8 SDK provides two APIs to frontend application components:
 

From 3f813b47c3f4220a71c8c3a48db76b6b5fcb5436 Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Thu, 3 Aug 2023 14:52:33 -0400
Subject: [PATCH 05/13] update kserve docs

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 .../integrations/kserve/blue-green.md         | 125 +++++++++---------
 docs/tutorials/integrations/kserve/canary.md  | 105 +++++++--------
 samples/kserve-serving/sleep.sh               |  29 +++-
 3 files changed, 136 insertions(+), 123 deletions(-)

diff --git a/docs/tutorials/integrations/kserve/blue-green.md b/docs/tutorials/integrations/kserve/blue-green.md
index 2fcba181..025a4179 100644
--- a/docs/tutorials/integrations/kserve/blue-green.md
+++ b/docs/tutorials/integrations/kserve/blue-green.md
@@ -6,7 +6,7 @@ template: main.html
 
 This tutorial shows how Iter8 can be used to implement a blue-green rollout of ML models hosted in a KServe environment. In a blue-green rollout, a percentage of inference requests are directed to a candidate version of the model. The remaining requests go to the primary, or initial, version of the model. Iter8 enables a blue-green rollout by automatically configuring routing resources to distribute inference requests.
 
-After a one-time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Optionally, the end user can modify the percentage of inference requests being sent to the candidate model. Iter8 automatically handles all underlying network configuration.
+After a one-time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Optionally, the end user can modify the percentage of inference requests being sent to the candidate model. Iter8 automatically handles all underlying routing configuration.
 
 ![Blue-Green rollout](images/blue-green.png)
 
@@ -16,20 +16,17 @@ After a one-time initialization step, the end user merely deploys candidate mode
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.10/hack/quick_install.sh" | bash
     ```
-<!-- Istio 1.15 already installed 
-    3. Install [Istio](https://istio.io). You can install the [demo profile](https://istio.io/latest/docs/setup/getting-started/).
-    ```shell
-    istioctl install --set profile=demo -y
-    ```
--->
+<!-- Istio 1.15 is installed as part of kserve install -->
 
-## Install the Iter8 controller
+## Install Iter8
 
 --8<-- "docs/tutorials/installiter8controller.md"
 
-## Deploy a primary model
+## Initialize primary
+
+### Application
 
-Deploy the primary version of a model using an `InferenceService`:
+Deploy the primary version of the application. In this tutorial, the application is a KServe model. Initialize the resources for the primary version of the model (`v0`) by deploying an `InferenceService` as follows:
 
 ```shell
 cat <<EOF | kubectl apply -f -
@@ -52,38 +49,38 @@ spec:
 EOF
 ```
 
-??? note "About the primary `InferenceService`"
-    Naming the model with the suffix `-0` (and the candidate with the suffix `-1`) simplifies the rollout initialization. However, any name can be specified.
+??? note "About the primary"
+    The base name (`wisdom`) and version (`v0`) are identified using the labels `app.kubernets.io/name` and `app.kubernets.io.version`, respectively. These labels are not required.
+
+    Naming the instance with the suffix `-0` (and the candidate with the suffix `-1`) simplifies the routing initialization (see below). However, any name can be specified.
     
-    The label `iter8.tools/watch: "true"` lets Iter8 know that it should pay attention to changes to this `InferenceService`.
+    The label `iter8.tools/watch: "true"` is required. It lets Iter8 know that it should pay attention to changes to this application resource.
 
-Inspect the deployed `InferenceService`:
+You can inspect the deployed `InferenceService`. When the `READY` field becomes `True`, the model is fully deployed.
 
 ```shell
 kubectl get inferenceservice wisdom-0
 ```
 
-When the `READY` field becomes `True`, the model is fully deployed.
-    
-## Initialize the Blue-Green routing policy
+### Routing
 
-Initialize model rollout with a blue-green traffic pattern as follows:
+Initialize the routing resources for the application to use a blue-green rollout strategy:
 
 ```shell
-#cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl apply -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/traffic-templates -f - | kubectl apply -f -
-templateName: initialize-rollout
-targetEnv: kserve
-trafficStrategy: blue-green
-modelName: wisdom
+#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl apply -f -
+appType: kserve
+appName: wisdom
+action: initialize
+strategy: blue-green
 EOF
 ```
 
-The `initialize-rollout` template (with `trafficStrategy: blue-green`) configures the Istio service mesh to route all requests to the primary version of the model (`wisdom-0`). Further, it defines the routing policy that will be used by Iter8 when it observes changes in the models. By default, this routing policy splits inference requests 50-50 between the primary and candidate versions. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/traffic/values.yaml).
+The `initialize` action (with strategy `blue-green`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It futher defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy splits requests 50-50 between the primary and candidate versions. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
 
-## Verify network configuration
+## Verify routing
 
-To verify the network configuration, you can inspect the network configuration:
+To verify the routing configuration, you can inspect the `VirtualService`:
 
 ```shell
 kubectl get virtualservice -o yaml wisdom
@@ -123,12 +120,12 @@ To send inference requests to the model:
     3. Send inference requests:
       ```shell
       curl -H 'Content-Type: application/json' -H 'Host: wisdom.default' localhost:8080 -d @input.json -s -D - \
-      | grep -e HTTP -e mm-vmodel-id
+      | grep -e HTTP -e app-version
       ```
 
-Note that the model version responding to each inference request is noted in the response header `mm-vmodel-id`. In the requests above, we display only the response code and this header.
+Note that the model version responding to each inference request is noted in the response header `app-version`. In the requests above, we display  only the response code and this header.
 
-## Deploy a candidate model
+## Deploy candidate
 
 Deploy a candidate model using a second `InferenceService`:
 
@@ -153,47 +150,45 @@ spec:
 EOF
 ```
 
-??? note "About the candidate `InferenceService`"
-    The model name (`wisdom`) and version (`v1`) are recorded using the labels `app.kubernets.io/name` and `app.kubernets.io.version`.
-
+??? note "About the candidate"
     In this tutorial, the model source (field `spec.predictor.model.storageUri`) is the same as for the primary version of the model. In a real world example, this would be different.
 
-## Verify network configuration changes
+## Verify routing changes
 
-The deployment of the candidate model triggers an automatic reconfiguration by Iter8. Inspect the `VirtualService` to see that inference requests are now distributed between the primary model and the secondary model:
+The deployment of the candidate triggers an automatic routing reconfiguration by Iter8. Inspect the `VirtualService` to see that inference requests are now distributed between the primary model and the secondary model:
 
 ```shell
 kubectl get virtualservice wisdom -o yaml
 ```
 
-Further, you can send additional inference requests as described above. They will be handled by both versions of the model.
+You can send additional inference requests as described above. They will be handled by both versions of the model.
 
 ## Modify weights (optional)
 
-You can modify the weight distribution of inference requests using the Iter8 `traffic-template` chart:
+You can modify the weight distribution of inference requests as follows:
 
 ```shell
-#cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl apply -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/traffic-templates -f - | kubectl apply -f -
-templateName: modify-weights
-targetEnv: kserve
-trafficStrategy: blue-green
-modelName: wisdom
-modelVersions:
+#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl apply -f -
+appType: kserve
+appName: wisdom
+action: modify-weights
+strategy: blue-green
+appVersions:
   - weight: 20
   - weight: 80
 EOF
 ```
 
-Note that using the `modify-weights` overrides the default traffic split for all future candidate deployments.
+Note that using the `modify-weights` action overrides the default traffic split for all future candidate deployments.
 
-As above, you can verify the network configuration changes.
+As above, you can verify the routing changes.
 
-## Promote the candidate model
+## Promote candidate
 
-Promoting the candidate involves redefining the primary `InferenceService` using the new model and deleting the candidate `InferenceService`.
+Promoting the candidate involves redefining the primary version of the application and deleting the candidate version.
 
-### Redefine the primary `InferenceService`
+### Redefine primary
 
 ```shell
 cat <<EOF | kubectl replace -f -
@@ -219,7 +214,7 @@ EOF
 ??? note "What is different?"
     The version label (`app.kubernets.io/version`) was updated. In a real world example, `spec.predictor.model.storageUri` would also be updated.
 
-### Delete the candidate `InferenceService`
+### Delete candidate
 
 Once the primary `InferenceService` has been redeployed, delete the candidate:
 
@@ -227,36 +222,36 @@ Once the primary `InferenceService` has been redeployed, delete the candidate:
 kubectl delete inferenceservice wisdom-1
 ```
 
-### Verify network configuration changes
+### Verify routing changes
 
-Inspect the `VirtualService` to see that the it has been automatically reconfigured to send requests only to the primary model.
+Inspect the `VirtualService` to see that the it has been automatically reconfigured to send requests only to the primary.
 
-## Clean up
+## Cleanup
 
-Delete the candidate model:
+If not alredy deleted, delete the candidate:
 
 ```shell
-kubectl delete --force isvc/wisdom-1
+kubectl delete isvc/wisdom-1
 ```
 
-Delete routing artifacts:
+Delete routing:
 
 ```shell
-#cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl delete --force -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/traffic-templates -f - | kubectl apply -f -
-templateName: initialize-rollout
-targetEnv: kserve
-trafficStrategy: blue-green
-modelName: wisdom
+#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl delete -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl delete -f -
+appType: kserve
+appName: wisdom
+action: initialize
+strategy: blue-green
 EOF
 ```
 
-Delete the primary model:
+Delete the primary:
 
 ```shell
-kubectl delete --force isvc/wisdom-0
+kubectl delete isvc/wisdom-0
 ```
 
-Uninstall the Iter8 controller:
+Uninstall Iter8:
 
 --8<-- "docs/tutorials/deleteiter8controller.md"
diff --git a/docs/tutorials/integrations/kserve/canary.md b/docs/tutorials/integrations/kserve/canary.md
index e4e36a27..db42d2cb 100644
--- a/docs/tutorials/integrations/kserve/canary.md
+++ b/docs/tutorials/integrations/kserve/canary.md
@@ -4,9 +4,9 @@ template: main.html
 
 # Canary Rollout of a KServe ML Model
 
-This tutorial shows how Iter8 can be used to implement a canary rollout of ML models hosted in a KServe environment. In a canary rollout, inference requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. Iter8 enables a canary rollout by automatically configuring the network to distribute inference requests.
+This tutorial shows how Iter8 can be used to implement a canary rollout of ML models hosted in a KServe environment. In a canary rollout, inference requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. Iter8 enables a canary rollout by automatically configuring the routing resources to distribute inference requests.
 
-After a one-time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Iter8 automatically handles the underlying network configuration.
+After a one-time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Iter8 automatically handles the underlying routing configuration.
 
 ![Canary rollout](images/canary.png)
 
@@ -16,20 +16,17 @@ After a one-time initialization step, the end user merely deploys candidate mode
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.10/hack/quick_install.sh" | bash
     ```
-<!-- Istio 1.15 already installed 
-    3. Install [Istio](https://istio.io). You can install the [demo profile](https://istio.io/latest/docs/setup/getting-started/).
-    ```shell
-    istioctl install --set profile=demo -y
-    ```
--->
+<!-- Istio 1.15 is installed as part of kserve install -->
 
-## Install the Iter8 controller
+## Install Iter8
 
 --8<-- "docs/tutorials/installiter8controller.md"
 
-## Deploy a primary model
+## Initialize primary
+
+### Application
 
-Deploy the primary version of a model using an `InferenceService`:
+Deploy the primary version of the application. In this tutorial, the application is a KServe model. Initialize the resources for the primary version of the model (`v0`) by deploying an `InferenceService` as follows:
 
 ```shell
 cat <<EOF | kubectl apply -f -
@@ -53,37 +50,37 @@ EOF
 ```
 
 ??? note "About the primary `InferenceService`"
-    Naming the model with the suffix `-0` (and the candidate with the suffix `-1`) simplifies the rollout initialization. However, any name can be specified.
+    The base name (`wisdom`) and version (`v0`) are identified using the labels `app.kubernets.io/name` and `app.kubernets.io.version`, respectively. These labels are not required.
+
+    Naming the instance with the suffix `-0` (and the candidate with the suffix `-1`) simplifies the routing initialization (see below). However, any name can be specified.
     
-    The label `iter8.tools/watch: "true"` lets Iter8 know that it should pay attention to changes to this `InferenceService`.
+    The label `iter8.tools/watch: "true"` is required. It lets Iter8 know that it should pay attention to changes to this application resource.
 
-Inspect the deployed `InferenceService`:
+You can inspect the deployed `InferenceService`. When the `READY` field becomes `True`, the model is fully deployed.
 
 ```shell
 kubectl get inferenceservice wisdom-0
 ```
-
-When the `READY` field becomes `True`, the model is fully deployed.
     
-## Initialize the Canary routing policy
+### Routing
 
-Initialize model rollout with a canary traffic pattern as follows:
+Initialize the routing resources for the application to use a canary rollout strategy:
 
 ```shell
-#cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl apply -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/traffic-templates -f - | kubectl apply -f -
-templateName: initialize-rollout
-targetEnv: kserve
-trafficStrategy: canary
-modelName: wisdom
+#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl apply -f -
+appType: kserve
+appName: wisdom
+action: initialize
+strategy: canary
 EOF
 ```
 
-The `initialize-rollout` template (with `trafficStrategy: canary`) configures the service mesh to route all requests to the primary version of the model (`wisdom-0`). Further, it defines the routing policy that will be used by Iter8 when it observes changes in the models. By default, this routing policy sends inference requests with the header `traffic` set to the value `test` to the candidate version of the model and all remaining inference requests to the primary version of the model. For detailed configuration options, see the Helm chart.
+The `initialize` action (with strategy `canary`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes in the application resources. By default, this routing policy sends requests with the header `traffic` set to the value `test` to the candidate version and all remaining requests to the primary version. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
 
-## Verify network configuration
+## Verify routing
 
-To verify the network configuration, you can inspect the network configuration:
+To verify the routing configuration, you can inspect the `VirtualService`:
 
 ```shell
 kubectl get virtualservice -o yaml wisdom
@@ -128,18 +125,18 @@ To send inference requests to the model:
     3. Send inference requests:
       ```shell
       curl -H 'Content-Type: application/json' -H 'Host: wisdom.default' localhost:8080 -d @input.json -s -D - \
-      | grep -e HTTP -e mm-vmodel-id
+      | grep -e HTTP -e app-version
       ```
     or, to send a request with header `traffic: test`:
     ```shell
       curl -H 'Content-Type: application/json' -H 'Host: wisdom.default' localhost:8080 -d @input.json -s -D - \
       -H 'traffic: test' \
-      | grep -e HTTP -e mm-vmodel-id
+      | grep -e HTTP -e app-version
     ```
 
-Note that the model version responding to each inference request is noted in the response header `mm-vmodel-id`. In the requests above, we display only the response code and this header.
+Note that the model version responding to each inference request is noted in the response header `app-version`. In the requests above, we display only the response code and this header.
 
-## Deploy a candidate model
+## Deploy candidate
 
 Deploy a candidate model using a second `InferenceService`:
 
@@ -165,25 +162,23 @@ EOF
 ```
 
 ??? note "About the candidate `InferenceService`"
-    The model name (`wisdom`) and version (`v1`) are recorded using the labels `app.kubernets.io/name` and `app.kubernets.io.version`.
-
     In this tutorial, the model source (field `spec.predictor.model.storageUri`) is the same as for the primary version of the model. In a real world example, this would be different.
 
-## Verify network configuration changes
+## Verify routing changes
 
-The deployment of the candidate model triggers an automatic reconfiguration by Iter8. Inspect the `VirtualService` to see that inference requests are now distributed between the primary model and the secondary model:
+The deployment of the candidate triggers an automatic routing reconfiguration by Iter8. Inspect the `VirtualService` to see that inference requests are now distributed between the primary model and the secondary model:
 
 ```shell
 kubectl get virtualservice wisdom -o yaml
 ```
 
-Further, you can send additional inference requests as described above. They will be handled by both versions of the model.
+You can send additional inference requests as described above. They will be handled by both versions of the model.
 
-## Promote the candidate model
+## Promote candidate
 
-Promoting the candidate involves redefining the primary `InferenceService` using the new model and deleting the candidate `InferenceService`.
+Promoting the candidate involves redefining the primary version of the application and deleting the candidate version.
 
-### Redefine the primary `InferenceService`
+### Redefine primary
 
 ```shell
 cat <<EOF | kubectl replace -f -
@@ -209,7 +204,7 @@ EOF
 ??? note "What is different?"
     The version label (`app.kubernets.io/version`) was updated. In a real world example, `spec.predictor.model.storageUri` would also be updated.
 
-### Delete the candidate `InferenceService`
+### Delete candidate
 
 Once the primary `InferenceService` has been redeployed, delete the candidate:
 
@@ -217,36 +212,36 @@ Once the primary `InferenceService` has been redeployed, delete the candidate:
 kubectl delete inferenceservice wisdom-1
 ```
 
-### Verify network configuration changes
+### Verify routing changes
 
-Inspect the `VirtualService` to see that the it has been automatically reconfigured to send requests only to the primary model.
+Inspect the `VirtualService` to see that the it has been automatically reconfigured to send requests only to the primary.
 
-## Clean up
+## Cleanup
 
-Delete the candidate model:
+If not already deleted, delete the candidate model:
 
 ```shell
-kubectl delete --force isvc/wisdom-1
+kubectl delete isvc/wisdom-1
 ```
 
-Delete routing artifacts:
+Delete routing:
 
 ```shell
-#cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl delete --force -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/traffic-templates -f - | kubectl delete -f -
-templateName: initialize-rollout
-targetEnv: kserve
-trafficStrategy: canary
-modelName: wisdom
+#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl delete -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl delete -f -
+appType: kserve
+appName: wisdom
+action: initialize
+strategy: canary
 EOF
 ```
 
-Delete the primary model:
+Delete the primary:
 
 ```shell
-kubectl delete --force isvc/wisdom-0
+kubectl delete isvc/wisdom-0
 ```
 
-Uninstall the Iter8 controller:
+Uninstall Iter8:
 
 --8<-- "docs/tutorials/deleteiter8controller.md"
diff --git a/samples/kserve-serving/sleep.sh b/samples/kserve-serving/sleep.sh
index 263f4cb6..10bfbce1 100644
--- a/samples/kserve-serving/sleep.sh
+++ b/samples/kserve-serving/sleep.sh
@@ -1,5 +1,14 @@
 #!/bin/sh
-cat <<EOF | kubectl apply -f -
+
+# to use with RedHat OpenShift Service Mesh, set
+# SERVICE_MESH=servicemesh
+
+if [ -z ${SERVICE_MESH+x} ]; then
+  SERVICE_MESH="istio"
+fi
+
+MANIFEST=/tmp/manifest.$$
+cat <<EOF > $MANIFEST
 apiVersion: apps/v1
 kind: Deployment
 metadata:
@@ -13,7 +22,18 @@ spec:
     metadata:
       labels:
         app: sleep
+EOF
+if [ "${SERVICE_MESH}" = "istio" ]; then
+cat <<EOF >> $MANIFEST
+        sidecar.istio.io/inject: "true"
+EOF
+elif [ "${SERVICE_MESH}" = "servicemesh" ]; then
+cat <<EOF >> $MANIFEST
+      annotations:
         sidecar.istio.io/inject: "true"
+EOF
+fi
+cat <<EOF >> $MANIFEST
     spec:
       containers:
       - name: sleep
@@ -54,9 +74,12 @@ data:
     }
   wisdom.sh: |
     curl -H 'Content-Type: application/json' http://wisdom.default -d @input.json -s -D - \
-    | grep -e HTTP -e mm-vmodel-id
+    | grep -e HTTP -e app-version
   wisdom-test.sh: |
     curl -H 'Content-Type: application/json' http://wisdom.default -d @input.json -s -D - \
     -H 'traffic: test' \
-    | grep -e HTTP -e mm-vmodel-id
+    | grep -e HTTP -e app-version
 EOF
+
+kubectl apply -f $MANIFEST
+rm -f $MANIFEST

From 80f77d4a88d7a8400751da4b2a66e1a7814e8e22 Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Thu, 3 Aug 2023 17:04:11 -0400
Subject: [PATCH 06/13] update kserver-modelmesh use cases

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 docs/tutorials/deleteiter8controller.md       |   4 -
 docs/tutorials/installiter8controller.md      |   4 -
 .../integrations/kserve-mm/blue-green.md      | 120 ++---
 .../integrations/kserve-mm/canary.md          | 110 ++---
 .../integrations/kserve/blue-green.md         |  10 +-
 docs/tutorials/integrations/kserve/canary.md  |  10 +-
 samples/modelmesh-serving/sleep.sh            | 426 ++++++++++++++++++
 7 files changed, 559 insertions(+), 125 deletions(-)

diff --git a/docs/tutorials/deleteiter8controller.md b/docs/tutorials/deleteiter8controller.md
index ef94a7d4..527b53bb 100644
--- a/docs/tutorials/deleteiter8controller.md
+++ b/docs/tutorials/deleteiter8controller.md
@@ -1,13 +1,9 @@
 === "Helm"
-    Delete the Iter8 controller using `helm` as follows.
-
     ```shell
     helm delete iter8
     ```
     
 === "Kustomize"
-    Delete the Iter8 controller using `kustomize` as follows.
-
     === "namespace scoped"
         ```shell
         kubectl delete -k 'https://github.com/iter8-tools/iter8.git/kustomize/iter8/namespaceScoped?ref=v0.15.3'
diff --git a/docs/tutorials/installiter8controller.md b/docs/tutorials/installiter8controller.md
index 5e1915af..158403b4 100644
--- a/docs/tutorials/installiter8controller.md
+++ b/docs/tutorials/installiter8controller.md
@@ -1,6 +1,4 @@
 === "Helm"
-    Install the Iter8 controller using `helm` as follows.
-
     === "namespace scoped"
         ```shell
         helm install --repo https://iter8-tools.github.io/iter8 iter8 traffic
@@ -13,8 +11,6 @@
         ```
     
 === "Kustomize"
-    Install the Iter8 controller using `kustomize` as follows.
-
     === "namespace scoped"
         ```shell
         kubectl apply -k 'https://github.com/iter8-tools/iter8.git/kustomize/iter8/namespaceScoped?ref=v0.15.3'
diff --git a/docs/tutorials/integrations/kserve-mm/blue-green.md b/docs/tutorials/integrations/kserve-mm/blue-green.md
index f92cc76e..c2dc8234 100644
--- a/docs/tutorials/integrations/kserve-mm/blue-green.md
+++ b/docs/tutorials/integrations/kserve-mm/blue-green.md
@@ -4,9 +4,9 @@ template: main.html
 
 # Blue-Green Rollout of a ML Model
 
-This tutorial shows how Iter8 can be used to implement a blue-green rollout of ML models hosted in a KServe modelmesh serving environment. In a blue-green rollout, a percentage of inference requests are directed to a candidate version of the model. The remaining requests go to the primary, or initial, version of the model. Iter8 enables a blue-green rollout by automatically configuring the network to distribute inference requests.
+This tutorial shows how Iter8 can be used to implement a blue-green rollout of ML models hosted in a KServe modelmesh serving environment. In a blue-green rollout, a percentage of inference requests are directed to a candidate version of the model. The remaining requests go to the primary, or initial, version of the model. Iter8 enables a blue-green rollout by automatically configuring routing resources to distribute inference requests.
 
-After a one time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Optionally, the end user can modify the percentage of inference requests being sent to the candidate model. Iter8 automatically handles all underlying network configuration.
+After a one time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Optionally, the end user can modify the percentage of inference requests being sent to the candidate model. Iter8 automatically handles all underlying routing configuration.
 
 ![Blue-Green rollout](images/blue-green.png)
 
@@ -14,16 +14,21 @@ In this tutorial, we use the Istio service mesh to distribute inference requests
 
 ???+ "Before you begin"
     1. Ensure that you have the [kubectl CLI](https://kubernetes.io/docs/reference/kubectl/).
-    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/main/docs/quickstart.md) environment.
+    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, change your default namespece to `modelmesh-serving`: 
+    ```shell
+    kubectl config set-context --current --namespace=modelmesh-serving
+    ```
     3. Install [Istio](https://istio.io). You can install the [demo profile](https://istio.io/latest/docs/setup/getting-started/).
 
-## Install the Iter8 controller
+## Install Iter8
 
 --8<-- "docs/tutorials/installiter8controller.md"
 
-## Deploy a primary model
+## Initialize primary
+
+### Application
 
-Deploy the primary version of a model using an `InferenceService`:
+Deploy the primary version of the application. In this tutorial, the application is an ML model. Initialize the resources for the primary version of the model (`v0`) by deploying an `InferenceService` as follows:
 
 ```shell
 cat <<EOF | kubectl apply -f -
@@ -48,36 +53,37 @@ EOF
 ```
 
 ??? note "About the primary `InferenceService`"
-    Naming the model with the suffix `-0` (and the candidate with the suffix `-1`) simplifies the rollout initialization. However, any name can be specified.
+    The base name (`wisdom`) and version (`v0`) are identified using the labels `app.kubernets.io/name` and `app.kubernets.io.version`, respectively. These labels are not required.
+
+    Naming the instance with the suffix `-0` (and the candidate with the suffix `-1`) simplifies the routing initialization (see below). However, any name can be specified.
     
-    The label `iter8.tools/watch: "true"` lets Iter8 know that it should pay attention to changes to this `InferenceService`.
+    The label `iter8.tools/watch: "true"` is required. It lets Iter8 know that it should pay attention to changes to this application resource.
 
-Inspect the deployed `InferenceService`:
+You can inspect the deployed `InferenceService`. When the `READY` field becomes `True`, the model is fully deployed.
 
 ```shell
 kubectl get inferenceservice wisdom-0
 ```
-
-When the `READY` field becomes `True`, the model is fully deployed.
     
-## Initialize the Blue-Green routing policy
+### Routing
 
 Initialize model rollout with a blue-green traffic pattern as follows:
 
 ```shell
-cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl apply -f -
-templateName: initialize-rollout
-targetEnv: kserve-modelmesh
-trafficStrategy: blue-green
-modelName: wisdom
+#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl apply -f -
+appType: kserve-modelmesh
+appName: wisdom
+action: initialize
+strategy: blue-green
 EOF
 ```
 
-The `initialize-rollout` template (with `trafficStrategy: blue-green`) configures the Istio service mesh to route all requests to the primary version of the model (`wisdom-0`). Further, it defines the routing policy that will be used by Iter8 when it observes changes in the models. By default, this routing policy splits inference requests 50-50 between the primary and candidate versions. For detailed configuration options, see the Helm chart.
+The `initialize` action (with strategy `blue-green`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It futher defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy splits requests 50-50 between the primary and candidate versions. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
 
-## Verify network configuration
+## Verify routing
 
-To verify the network configuration, you can inspect the network configuration:
+To verify the routing configuration, you can inspect the `VirtualService`:
 
 ```shell
 kubectl get virtualservice -o yaml wisdom
@@ -88,7 +94,8 @@ To send inference requests to the model:
 === "From within the cluster"
     1. Create a "sleep" pod in the cluster from which requests can be made:
     ```shell
-    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.14.3/samples/modelmesh-serving/sleep.sh | sh -
+    # curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/modelmesh-serving/sleep.sh | sh -
+    source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/modelmesh-serving/sleep.sh
     ```
 
     2. exec into the sleep pod:
@@ -118,14 +125,15 @@ To send inference requests to the model:
     3. Send inference requests:
       ```shell
       cat grpc_input.json | \
-      grpcurl -plaintext -proto kserve.proto -d @ \
+      grpcurl -vv -plaintext -proto kserve.proto -d @ \
       -authority wisdom.modelmesh-serving \
-      localhost:8080 inference.GRPCInferenceService.ModelInfer
+      localhost:8080 inference.GRPCInferenceService.ModelInfer \
+      | grep -e app-version
       ```
 
-Note that the model version responding to each inference request is noted in the response header `mm-vmodel-id`. In the requests above, we display only the response code and this header.
+Note that the model version responding to each inference request is noted in the response header `app-version`. In the requests above, we display only this header.
 
-## Deploy a candidate model
+## Deploy candidate
 
 Deploy a candidate model using a second `InferenceService`:
 
@@ -152,45 +160,44 @@ EOF
 ```
 
 ??? note "About the candidate `InferenceService`"
-    The model name (`wisdom`) and version (`v1`) are recorded using the labels `app.kubernets.io/name` and `app.kubernets.io.version`.
-
     In this tutorial, the model source (field `spec.predictor.model.storageUri`) is the same as for the primary version of the model. In a real world example, this would be different.
 
-## Verify network configuration changes
+## Verify routing changes
 
-The deployment of the candidate model triggers an automatic reconfiguration by Iter8. Inspect the `VirtualService` to see that inference requests are now distributed between the primary model and the secondary model:
+The deployment of the candidate model triggers an automatic reconfiguration by Iter8. Inspect the `VirtualService` to see that the routing has been changed. Requests are now distributed between the primary and candidate:
 
 ```shell
 kubectl get virtualservice wisdom -o yaml
 ```
 
-Further, you can send additional inference requests as described above. They will be handled by both versions of the model.
+You can send additional inference requests as described above. They will be handled by both versions of the model.
 
 ## Modify weights (optional)
 
-You can modify the weight distribution of inference requests using the Iter8 `traffic-template` chart:
+You can modify the weight distribution of inference requests as follows:
 
 ```shell
-cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl apply -f -
-templateName: modify-weights
-targetEnv: kserve-modelmesh
-trafficStrategy: blue-green
-modelName: wisdom
-modelVersions:
+#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl apply -f -
+appType: kserve-modelmesh
+appName: wisdom
+action: modify-weights
+strategy: blue-green
+appVersions:
   - weight: 20
   - weight: 80
 EOF
 ```
 
-Note that using the `modify-weights` overrides the default traffic split for all future candidate deployments.
+Note that using the `modify-weights` action overrides the default traffic split for all future candidate deployments.
 
-As above, you can verify the network configuration changes.
+As above, you can verify the routing changes.
 
-## Promote the candidate model
+## Promote candidate
 
-Promoting the candidate involves redefining the primary `InferenceService` using the new model and deleting the candidate `InferenceService`.
+Promoting the candidate involves redefining the primary version of the application and deleting the candidate version.
 
-### Redefine the primary `InferenceService`
+### Redefine primary
 
 ```shell
 cat <<EOF | kubectl replace -f -
@@ -217,7 +224,7 @@ EOF
 ??? note "What is different?"
     The version label (`app.kubernets.io/version`) was updated. In a real world example, `spec.predictor.model.storageUri` would also be updated.
 
-### Delete the candidate `InferenceService`
+### Delete candidate
 
 Once the primary `InferenceService` has been redeployed, delete the candidate:
 
@@ -225,35 +232,36 @@ Once the primary `InferenceService` has been redeployed, delete the candidate:
 kubectl delete inferenceservice wisdom-1
 ```
 
-### Verify network configuration changes
+### Verify routing changes
 
 Inspect the `VirtualService` to see that the it has been automatically reconfigured to send requests only to the primary model.
 
-## Clean up
+## Cleanup
 
-Delete the candidate model:
+If not already deleted, delete the candidate:
 
 ```shell
-kubectl delete --force isvc/wisdom-1
+kubectl delete isvc/wisdom-1
 ```
 
-Delete routing artifacts:
+Delete routing:
 
 ```shell
-cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl delete --force -f -
-templateName: initialize-rollout
-targetEnv: kserve-modelmesh
-trafficStrategy: blue-green
-modelName: wisdom
+#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl delete -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl delete -f -
+appType: kserve-modelmesh
+appName: wisdom
+action: initialize
+strategy: blue-green
 EOF
 ```
 
-Delete the primary model:
+Delete primary:
 
 ```shell
-kubectl delete --force isvc/wisdom-0
+kubectl delete isvc/wisdom-0
 ```
 
-Uninstall the Iter8 controller:
+Uninstall Iter8:
 
 --8<-- "docs/tutorials/deleteiter8controller.md"
diff --git a/docs/tutorials/integrations/kserve-mm/canary.md b/docs/tutorials/integrations/kserve-mm/canary.md
index 53f09b62..fdf00f3a 100644
--- a/docs/tutorials/integrations/kserve-mm/canary.md
+++ b/docs/tutorials/integrations/kserve-mm/canary.md
@@ -4,9 +4,9 @@ template: main.html
 
 # Canary Rollout of a ML Model
 
-This tutorial shows how Iter8 can be used to implement a canary rollout of ML models hosted in a KServe modelmesh serving environment. In a canary rollout, inference requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. Iter8 enables a canary rollout by automatically configuring the network to distribute inference requests.
+This tutorial shows how Iter8 can be used to implement a canary rollout of ML models hosted in a KServe modelmesh serving environment. In a canary rollout, inference requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. Iter8 enables a canary rollout by automatically configuring the routing resources to distribute inference requests.
 
-After a one time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Iter8 automatically handles the underlying network configuration.
+After a one time initialization step, the end user merely deploys candidate models, evaluates them, and either promotes or deletes them. Iter8 automatically handles the underlying routing configuration.
 
 ![Canary rollout](images/canary.png)
 
@@ -14,16 +14,21 @@ In this tutorial, we use the Istio service mesh to distribute inference requests
 
 ???+ "Before you begin"
     1. Ensure that you have the [kubectl CLI](https://kubernetes.io/docs/reference/kubectl/).
-    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/main/docs/quickstart.md) environment.
+    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, change your default namespece to `modelmesh-serving`: 
+    ```shell
+    kubectl config set-context --current --namespace=modelmesh-serving
+    ```
     3. Install [Istio](https://istio.io). You can install the [demo profile](https://istio.io/latest/docs/setup/getting-started/).
 
-## Install the Iter8 controller
+## Install Iter8
 
 --8<-- "docs/tutorials/installiter8controller.md"
 
-## Deploy a primary model
+## Initialize primary
+
+### Application
 
-Deploy the primary version of a model using an `InferenceService`:
+Deploy the primary version of the application. In this tutorial, the application is an ML model. Initialize the resources for the primary version of the model (`v0`) by deploying an `InferenceService` as follows:
 
 ```shell
 cat <<EOF | kubectl apply -f -
@@ -48,36 +53,37 @@ EOF
 ```
 
 ??? note "About the primary `InferenceService`"
-    Naming the model with the suffix `-0` (and the candidate with the suffix `-1`) simplifies the rollout initialization. However, any name can be specified.
+    The base name (`wisdom`) and version (`v0`) are identified using the labels `app.kubernets.io/name` and `app.kubernets.io.version`, respectively. These labels are not required.
+
+    Naming the instance with the suffix `-0` (and the candidate with the suffix `-1`) simplifies the routing initialization (see below). However, any name can be specified.
     
-    The label `iter8.tools/watch: "true"` lets Iter8 know that it should pay attention to changes to this `InferenceService`.
+    The label `iter8.tools/watch: "true"` is required. It lets Iter8 know that it should pay attention to changes to this application resource.
 
-Inspect the deployed `InferenceService`:
+You can inspect the deployed `InferenceService`. When the `READY` field becomes `True`, the model is fully deployed.
 
 ```shell
 kubectl get inferenceservice wisdom-0
 ```
 
-When the `READY` field becomes `True`, the model is fully deployed.
-
-## Initialize the Canary routing policy
+### Routing
 
-Initialize model rollout with a canary traffic pattern as follows:
+Initialize the routing resources for the application to use a canary rollout strategy:
 
 ```shell
-cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl apply -f -
-templateName: initialize-rollout
-targetEnv: kserve-modelmesh
-trafficStrategy: canary
-modelName: wisdom
+#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl apply -f -
+appType: kserve-modelmesh
+appName: wisdom
+action: initialize
+strategy: canary
 EOF
 ```
 
-The `initialize-rollout` template (with `trafficStrategy: canary`) configures the Istio service mesh to route all requests to the primary version of the model (`wisdom-0`). Further, it defines a routing policy that will be used by Iter8 when it observes changes in the models. By default, this routing policy sends inference requests with the header `traffic` set to the value `test` to the candidate version of the model and all remaining inference requests to the primary version of the model. For detailed configuration options, see the Helm chart.
+The `initialize` action (with strategy `canary`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It futher defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy sends requests with the header `traffic` set to the value `test` to the candidate version and all remaining requests to the primary version. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
 
-## Verify network configuration
+## Verify routing
 
-To verify the network configuration, you can inspect the network configuration:
+To verify the routing configuration, you can inspect the `VirtualService`:
 
 ```shell
 kubectl get virtualservice -o yaml wisdom
@@ -88,7 +94,8 @@ To send inference requests to the model:
 === "From within the cluster"
     1. Create a "sleep" pod in the cluster from which requests can be made:
     ```shell
-    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.14.3/samples/modelmesh-serving/sleep.sh | sh -
+    # curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/modelmesh-serving/sleep.sh | sh -
+    source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/modelmesh-serving/sleep.sh
     ```
 
     2. exec into the sleep pod:
@@ -123,22 +130,24 @@ To send inference requests to the model:
     3. Send inference requests:
       ```shell
       cat grpc_input.json | \
-      grpcurl -plaintext -proto kserve.proto -d @ \
+      grpcurl -vv -plaintext -proto kserve.proto -d @ \
       -authority wisdom.modelmesh-serving \
-      localhost:8080 inference.GRPCInferenceService.ModelInfer
+      localhost:8080 inference.GRPCInferenceService.ModelInfer \
+      | grep -e app-version
       ```
       or, to send a request with header `traffic: test`:
       ```shell
       cat grpc_input.json | \
-      grpcurl -plaintext -proto kserve.proto -d @ \
+      grpcurl -vv -plaintext -proto kserve.proto -d @ \
       -H 'traffic: test' \
       -authority wisdom.modelmesh-serving \
-      localhost:8080 inference.GRPCInferenceService.ModelInfer
+      localhost:8080 inference.GRPCInferenceService.ModelInfer \
+      | grep -e app-version
       ```
 
-Note that the model version responding to each inference request is noted in the response header `mm-vmodel-id`. In the requests above, we display only the response code and this header.
+Note that the model version responding to each inference request is noted in the response header `app-version`. In the requests above, we display only this header.
 
-## Deploy a candidate model
+## Deploy candidate
 
 Deploy a candidate model using a second `InferenceService`:
 
@@ -165,25 +174,23 @@ EOF
 ```
 
 ??? note "About the candidate `InferenceService`"
-    The model name (`wisdom`) and version (`v1`) are recorded using the labels `app.kubernets.io/name` and `app.kubernets.io.version`.
-
     In this tutorial, the model source (field `spec.predictor.model.storageUri`) is the same as for the primary version of the model. In a real example, this would be different.
 
-## Verify network configuration changes
+## Verify routing changes
 
-The deployment of the candidate model triggers an automatic reconfiguration by Iter8. Inspect the `VirtualService` to see that inference requests are now distributed between the primary model and the secondary model according to a header matching rule:
+The deployment of the candidate model triggers an automatic reconfiguration by Iter8. Inspect the `VirtualService` to see that the routing has been changed. Requests are now distributed between the primary and candidate:
 
 ```shell
 kubectl get virtualservice wisdom -o yaml
 ```
 
-Further, you can send additional inference requests as described above. They will be handled by both versions of the model.
+You can send additional inference requests as described above. They will be handled by both versions of the model.
 
-## Promote the candidate model
+## Promote candidate
 
-Promoting the candidate involves redefining the primary `InferenceService` using the new model and deleting the candidate `InferenceService`.
+Promoting the candidate involves redefining the primary version of the application and deleting the candidate version.
 
-### Redefine the primary `InferenceService`
+### Redefine primary
 
 ```shell
 cat <<EOF | kubectl replace -f -
@@ -210,7 +217,7 @@ EOF
 ??? note "What is different?"
     The version label (`app.kubernets.io/version`) was updated. In a real world example, `spec.predictor.model.storageUri` would also be updated.
 
-### Delete the candidate `InferenceService`
+### Delete candidate
 
 Once the primary `InferenceService` has been redeployed, delete the candidate:
 
@@ -218,35 +225,36 @@ Once the primary `InferenceService` has been redeployed, delete the candidate:
 kubectl delete inferenceservice wisdom-1
 ```
 
-### Verify network configuration changes
+### Verify routing changes
 
-Inspect the `VirtualService` to see that the it has been automatically reconfigured to send requests only to the primary model.
+Inspect the `VirtualService` to see that the it has been automatically reconfigured to send requests only to the primary.
 
-## Clean up
+## Cleanup
 
-Delete the candidate model:
+If not already deleted, delete the candidate model:
 
 ```shell
-kubectl delete --force isvc/wisdom-1
+kubectl delete isvc/wisdom-1
 ```
 
-Delete routing artifacts:
+Delete routing:
 
 ```shell
-cat <<EOF | helm template traffic --repo https://iter8-tools.github.io/iter8 traffic-templates -f - | kubectl delete --force -f -
-templateName: initialize-rollout
-targetEnv: kserve-modelmesh
-trafficStrategy: canary
-modelName: wisdom
+#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl delete -f -
+cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl delete -f -
+appType: kserve-modelmesh
+appName: wisdom
+action: initialize
+strategy: canary
 EOF
 ```
 
-Delete the primary model:
+Delete primary:
 
 ```shell
-kubectl delete --force isvc/wisdom-0
+kubectl delete isvc/wisdom-0
 ```
 
-Uninstall the Iter8 controller:
+Uninstall Iter8:
 
 --8<-- "docs/tutorials/deleteiter8controller.md"
diff --git a/docs/tutorials/integrations/kserve/blue-green.md b/docs/tutorials/integrations/kserve/blue-green.md
index 025a4179..6f6a207b 100644
--- a/docs/tutorials/integrations/kserve/blue-green.md
+++ b/docs/tutorials/integrations/kserve/blue-green.md
@@ -91,7 +91,7 @@ To send inference requests to the model:
 === "From within the cluster"
     1. Create a "sleep" pod in the cluster from which requests can be made:
     ```shell
-    # curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.0/samples/kserve-serving/sleep.sh | sh -
+    # curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/kserve-serving/sleep.sh | sh -
     source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/kserve-serving/sleep.sh
     ```
 
@@ -114,7 +114,7 @@ To send inference requests to the model:
 
     2. Download the sample input:
       ```shell
-      # curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.0/samples/kserve-serving/input.json
+      # curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/kserve-serving/input.json
       ```
 
     3. Send inference requests:
@@ -123,7 +123,7 @@ To send inference requests to the model:
       | grep -e HTTP -e app-version
       ```
 
-Note that the model version responding to each inference request is noted in the response header `app-version`. In the requests above, we display  only the response code and this header.
+Note that the model version responding to each inference request is noted in the response header `app-version`. In the requests above, we display only the response code and this header.
 
 ## Deploy candidate
 
@@ -155,7 +155,7 @@ EOF
 
 ## Verify routing changes
 
-The deployment of the candidate triggers an automatic routing reconfiguration by Iter8. Inspect the `VirtualService` to see that inference requests are now distributed between the primary model and the secondary model:
+The deployment of the candidate triggers an automatic routing reconfiguration by Iter8. Inspect the `VirtualService` to see that the routing has been changed. Inspect the `VirtualService` to see that the routing has been changed. Requests are now distributed between the primary model and the secondary model:
 
 ```shell
 kubectl get virtualservice wisdom -o yaml
@@ -246,7 +246,7 @@ strategy: blue-green
 EOF
 ```
 
-Delete the primary:
+Delete primary:
 
 ```shell
 kubectl delete isvc/wisdom-0
diff --git a/docs/tutorials/integrations/kserve/canary.md b/docs/tutorials/integrations/kserve/canary.md
index db42d2cb..0cefeb65 100644
--- a/docs/tutorials/integrations/kserve/canary.md
+++ b/docs/tutorials/integrations/kserve/canary.md
@@ -76,7 +76,7 @@ strategy: canary
 EOF
 ```
 
-The `initialize` action (with strategy `canary`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes in the application resources. By default, this routing policy sends requests with the header `traffic` set to the value `test` to the candidate version and all remaining requests to the primary version. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
+The `initialize` action (with strategy `canary`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It futher defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy sends requests with the header `traffic` set to the value `test` to the candidate version and all remaining requests to the primary version. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
 
 ## Verify routing
 
@@ -91,7 +91,7 @@ To send inference requests to the model:
 === "From within the cluster"
     1. Create a "sleep" pod in the cluster from which requests can be made:
     ```shell
-    # curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.0/samples/kserve-serving/sleep.sh | sh -
+    # curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/kserve-serving/sleep.sh | sh -
     source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/kserve-serving/sleep.sh
     ```
 
@@ -119,7 +119,7 @@ To send inference requests to the model:
 
     2. Download the sample input:
       ```shell
-      # curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.0/samples/kserve-serving/input.json
+      # curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/kserve-serving/input.json
       ```
 
     3. Send inference requests:
@@ -166,7 +166,7 @@ EOF
 
 ## Verify routing changes
 
-The deployment of the candidate triggers an automatic routing reconfiguration by Iter8. Inspect the `VirtualService` to see that inference requests are now distributed between the primary model and the secondary model:
+The deployment of the candidate triggers an automatic routing reconfiguration by Iter8. Inspect the `VirtualService` to see that the routing has been changed. Requests are now distributed between the primary model and the secondary model:
 
 ```shell
 kubectl get virtualservice wisdom -o yaml
@@ -236,7 +236,7 @@ strategy: canary
 EOF
 ```
 
-Delete the primary:
+Delete primary:
 
 ```shell
 kubectl delete isvc/wisdom-0
diff --git a/samples/modelmesh-serving/sleep.sh b/samples/modelmesh-serving/sleep.sh
index e69de29b..dac7e8c5 100644
--- a/samples/modelmesh-serving/sleep.sh
+++ b/samples/modelmesh-serving/sleep.sh
@@ -0,0 +1,426 @@
+<<<<<<< HEAD
+=======
+#!/bin/sh
+
+# To use with RedHat OpenShift Service Mesh, set
+# SERVICE_MESH=servicemesh
+
+if [ -z ${SERVICE_MESH+x} ]; then
+  SERVICE_MESH="istio"
+fi
+
+MANIFEST=/tmp/manifest.$$
+cat <<EOF > $MANIFEST
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: sleep
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: sleep
+  template:
+    metadata:
+      labels:
+        app: sleep
+EOF
+if [ "${SERVICE_MESH}" = "istio" ]; then
+cat <<EOF >> $MANIFEST
+        sidecar.istio.io/inject: "true"
+EOF
+elif [ "${SERVICE_MESH}" = "servicemesh" ]; then
+cat <<EOF >> $MANIFEST
+      annotations:
+        sidecar.istio.io/inject: "true"
+EOF
+fi
+cat <<EOF >> $MANIFEST
+    spec:
+      containers:
+      - name: sleep
+        image: fullstorydev/grpcurl:latest-alpine
+        command: ["/bin/sh", "-c", "sleep 3650d"]
+        workingDir: /demo
+        imagePullPolicy: IfNotPresent
+        volumeMounts:
+        - name: config-volume
+          mountPath: /demo
+        securityContext:
+          runAsNonRoot: true
+          runAsUser: 1001040000
+          allowPrivilegeEscalation: false
+      volumes:
+      - name: config-volume
+        configMap:
+          name: demo-input
+---
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: demo-input
+data:
+  kserve.proto: |
+    syntax = "proto3";
+    package inference;
+    option go_package = "github.com/kserve/modelmesh-serving/fvt/generated;inference";
+
+    // Inference Server GRPC endpoints.
+    service GRPCInferenceService
+    {
+      // The ServerLive API indicates if the inference server is able to receive
+      // and respond to metadata and inference requests.
+      rpc ServerLive(ServerLiveRequest) returns (ServerLiveResponse) {}
+
+      // The ServerReady API indicates if the server is ready for inferencing.
+      rpc ServerReady(ServerReadyRequest) returns (ServerReadyResponse) {}
+
+      // The ModelReady API indicates if a specific model is ready for inferencing.
+      rpc ModelReady(ModelReadyRequest) returns (ModelReadyResponse) {}
+
+      // The ServerMetadata API provides information about the server. Errors are
+      // indicated by the google.rpc.Status returned for the request. The OK code
+      // indicates success and other codes indicate failure.
+      rpc ServerMetadata(ServerMetadataRequest) returns (ServerMetadataResponse) {}
+
+      // The per-model metadata API provides information about a model. Errors are
+      // indicated by the google.rpc.Status returned for the request. The OK code
+      // indicates success and other codes indicate failure.
+      rpc ModelMetadata(ModelMetadataRequest) returns (ModelMetadataResponse) {}
+
+      // The ModelInfer API performs inference using the specified model. Errors are
+      // indicated by the google.rpc.Status returned for the request. The OK code
+      // indicates success and other codes indicate failure.
+      rpc ModelInfer(ModelInferRequest) returns (ModelInferResponse) {}
+    }
+
+    message ServerLiveRequest {}
+
+    message ServerLiveResponse
+    {
+      // True if the inference server is live, false if not live.
+      bool live = 1;
+    }
+
+    message ServerReadyRequest {}
+
+    message ServerReadyResponse
+    {
+      // True if the inference server is ready, false if not ready.
+      bool ready = 1;
+    }
+
+    message ModelReadyRequest
+    {
+      // The name of the model to check for readiness.
+      string name = 1;
+
+      // The version of the model to check for readiness. If not given the
+      // server will choose a version based on the model and internal policy.
+      string version = 2;
+    }
+
+    message ModelReadyResponse
+    {
+      // True if the model is ready, false if not ready.
+      bool ready = 1;
+    }
+
+    message ServerMetadataRequest {}
+
+    message ServerMetadataResponse
+    {
+      // The server name.
+      string name = 1;
+
+      // The server version.
+      string version = 2;
+
+      // The extensions supported by the server.
+      repeated string extensions = 3;
+    }
+
+    message ModelMetadataRequest
+    {
+      // The name of the model.
+      string name = 1;
+
+      // The version of the model to check for readiness. If not given the
+      // server will choose a version based on the model and internal policy.
+      string version = 2;
+    }
+
+    message ModelMetadataResponse
+    {
+      // Metadata for a tensor.
+      message TensorMetadata
+      {
+        // The tensor name.
+        string name = 1;
+
+        // The tensor data type.
+        string datatype = 2;
+
+        // The tensor shape. A variable-size dimension is represented
+        // by a -1 value.
+        repeated int64 shape = 3;
+      }
+
+      // The model name.
+      string name = 1;
+
+      // The versions of the model available on the server.
+      repeated string versions = 2;
+
+      // The model's platform. See Platforms.
+      string platform = 3;
+
+      // The model's inputs.
+      repeated TensorMetadata inputs = 4;
+
+      // The model's outputs.
+      repeated TensorMetadata outputs = 5;
+    }
+
+    message ModelInferRequest
+    {
+      // An input tensor for an inference request.
+      message InferInputTensor
+      {
+        // The tensor name.
+        string name = 1;
+
+        // The tensor data type.
+        string datatype = 2;
+
+        // The tensor shape.
+        repeated int64 shape = 3;
+
+        // Optional inference input tensor parameters.
+        map<string, InferParameter> parameters = 4;
+
+        // The tensor contents using a data-type format. This field must
+        // not be specified if "raw" tensor contents are being used for
+        // the inference request.
+        InferTensorContents contents = 5;
+      }
+
+      // An output tensor requested for an inference request.
+      message InferRequestedOutputTensor
+      {
+        // The tensor name.
+        string name = 1;
+
+        // Optional requested output tensor parameters.
+        map<string, InferParameter> parameters = 2;
+      }
+
+      // The name of the model to use for inferencing.
+      string model_name = 1;
+
+      // The version of the model to use for inference. If not given the
+      // server will choose a version based on the model and internal policy.
+      string model_version = 2;
+
+      // Optional identifier for the request. If specified will be
+      // returned in the response.
+      string id = 3;
+
+      // Optional inference parameters.
+      map<string, InferParameter> parameters = 4;
+
+      // The input tensors for the inference.
+      repeated InferInputTensor inputs = 5;
+
+      // The requested output tensors for the inference. Optional, if not
+      // specified all outputs produced by the model will be returned.
+      repeated InferRequestedOutputTensor outputs = 6;
+
+      // The data contained in an input tensor can be represented in "raw"
+      // bytes form or in the repeated type that matches the tensor's data
+      // type. To use the raw representation 'raw_input_contents' must be
+      // initialized with data for each tensor in the same order as
+      // 'inputs'. For each tensor, the size of this content must match
+      // what is expected by the tensor's shape and data type. The raw
+      // data must be the flattened, one-dimensional, row-major order of
+      // the tensor elements without any stride or padding between the
+      // elements. Note that the FP16 data type must be represented as raw
+      // content as there is no specific data type for a 16-bit float
+      // type.
+      //
+      // If this field is specified then InferInputTensor::contents must
+      // not be specified for any input tensor.
+      repeated bytes raw_input_contents = 7;
+    }
+
+    message ModelInferResponse
+    {
+      // An output tensor returned for an inference request.
+      message InferOutputTensor
+      {
+        // The tensor name.
+        string name = 1;
+
+        // The tensor data type.
+        string datatype = 2;
+
+        // The tensor shape.
+        repeated int64 shape = 3;
+
+        // Optional output tensor parameters.
+        map<string, InferParameter> parameters = 4;
+
+        // The tensor contents using a data-type format. This field must
+        // not be specified if "raw" tensor contents are being used for
+        // the inference response.
+        InferTensorContents contents = 5;
+      }
+
+      // The name of the model used for inference.
+      string model_name = 1;
+
+      // The version of the model used for inference.
+      string model_version = 2;
+
+      // The id of the inference request if one was specified.
+      string id = 3;
+
+      // Optional inference response parameters.
+      map<string, InferParameter> parameters = 4;
+
+      // The output tensors holding inference results.
+      repeated InferOutputTensor outputs = 5;
+
+      // The data contained in an output tensor can be represented in
+      // "raw" bytes form or in the repeated type that matches the
+      // tensor's data type. To use the raw representation 'raw_output_contents'
+      // must be initialized with data for each tensor in the same order as
+      // 'outputs'. For each tensor, the size of this content must match
+      // what is expected by the tensor's shape and data type. The raw
+      // data must be the flattened, one-dimensional, row-major order of
+      // the tensor elements without any stride or padding between the
+      // elements. Note that the FP16 data type must be represented as raw
+      // content as there is no specific data type for a 16-bit float
+      // type.
+      //
+      // If this field is specified then InferOutputTensor::contents must
+      // not be specified for any output tensor.
+      repeated bytes raw_output_contents = 6;
+    }
+
+    // An inference parameter value. The Parameters message describes a
+    // “name”/”value” pair, where the “name” is the name of the parameter
+    // and the “value” is a boolean, integer, or string corresponding to
+    // the parameter.
+    message InferParameter
+    {
+      // The parameter value can be a string, an int64, a boolean
+      // or a message specific to a predefined parameter.
+      oneof parameter_choice
+      {
+        // A boolean parameter value.
+        bool bool_param = 1;
+
+        // An int64 parameter value.
+        int64 int64_param = 2;
+
+        // A string parameter value.
+        string string_param = 3;
+      }
+    }
+
+    // The data contained in a tensor represented by the repeated type
+    // that matches the tensor's data type. Protobuf oneof is not used
+    // because oneofs cannot contain repeated fields.
+    message InferTensorContents
+    {
+      // Representation for BOOL data type. The size must match what is
+      // expected by the tensor's shape. The contents must be the flattened,
+      // one-dimensional, row-major order of the tensor elements.
+      repeated bool bool_contents = 1;
+
+      // Representation for INT8, INT16, and INT32 data types. The size
+      // must match what is expected by the tensor's shape. The contents
+      // must be the flattened, one-dimensional, row-major order of the
+      // tensor elements.
+      repeated int32 int_contents = 2;
+
+      // Representation for INT64 data types. The size must match what
+      // is expected by the tensor's shape. The contents must be the
+      // flattened, one-dimensional, row-major order of the tensor elements.
+      repeated int64 int64_contents = 3;
+
+      // Representation for UINT8, UINT16, and UINT32 data types. The size
+      // must match what is expected by the tensor's shape. The contents
+      // must be the flattened, one-dimensional, row-major order of the
+      // tensor elements.
+      repeated uint32 uint_contents = 4;
+
+      // Representation for UINT64 data types. The size must match what
+      // is expected by the tensor's shape. The contents must be the
+      // flattened, one-dimensional, row-major order of the tensor elements.
+      repeated uint64 uint64_contents = 5;
+
+      // Representation for FP32 data type. The size must match what is
+      // expected by the tensor's shape. The contents must be the flattened,
+      // one-dimensional, row-major order of the tensor elements.
+      repeated float fp32_contents = 6;
+
+      // Representation for FP64 data type. The size must match what is
+      // expected by the tensor's shape. The contents must be the flattened,
+      // one-dimensional, row-major order of the tensor elements.
+      repeated double fp64_contents = 7;
+
+      // Representation for BYTES data type. The size must match what is
+      // expected by the tensor's shape. The contents must be the flattened,
+      // one-dimensional, row-major order of the tensor elements.
+      repeated bytes bytes_contents = 8;
+    }
+  grpc_input.json: |
+    {
+      "inputs": [
+        { 
+          "name": "predict", 
+          "shape": [1, 64], 
+          "datatype": "FP32", 
+          "contents": { 
+            "fp32_contents": [0.0, 0.0, 1.0, 11.0, 14.0, 15.0, 3.0, 0.0, 0.0, 1.0, 13.0, 16.0, 12.0, 16.0, 8.0, 0.0, 0.0, 8.0, 16.0, 4.0, 6.0, 16.0, 5.0, 0.0, 0.0, 5.0, 15.0, 11.0, 13.0, 14.0, 0.0, 0.0, 0.0, 0.0, 2.0, 12.0, 16.0, 13.0, 0.0, 0.0, 0.0, 0.0, 0.0, 13.0, 16.0, 16.0, 6.0, 0.0, 0.0, 0.0, 0.0, 16.0, 16.0, 16.0, 7.0, 0.0, 0.0, 0.0, 0.0, 11.0, 13.0, 12.0, 1.0, 0.0]
+          }
+        }
+      ]
+    }
+  wisdom.sh: |
+    cat grpc_input.json | \
+    grpcurl -vv -plaintext -proto kserve.proto -d @ \
+    -authority wisdom.modelmesh-serving \
+    modelmesh-serving.modelmesh-serving:8033 \
+    inference.GRPCInferenceService.ModelInfer \
+    | grep -e app-version
+  wisdom-test.sh: |
+   cat grpc_input.json | \
+    grpcurl -vv -plaintext -proto kserve.proto -d @ \
+    -authority wisdom.modelmesh-serving \
+    -H 'traffic: test' \
+    modelmesh-serving.modelmesh-serving:8033 \
+    inference.GRPCInferenceService.ModelInfer \
+    | grep -e app-version
+  lightspeed.sh: |
+    cat grpc_input.json | \
+    grpcurl -vv -plaintext -proto kserve.proto -d @ \
+    -authority lightspeed.modelmesh-serving \
+    modelmesh-serving.modelmesh-serving:8033 \
+    inference.GRPCInferenceService.ModelInfer \
+    | grep -e app-version
+  lightspeed-test.sh: |
+    cat grpc_input.json | \
+    grpcurl -vv -plaintext -proto kserve.proto -d @ \
+    -authority lightspeed.modelmesh-serving \
+    -H 'traffic: test' \
+    modelmesh-serving.modelmesh-serving:8033 \
+    inference.GRPCInferenceService.ModelInfer \
+    | grep -e app-version
+EOF
+
+kubectl apply -f $MANIFEST
+rm -f $MANIFEST
+>>>>>>> 0d2649e (update kserver-modelmesh use cases)

From 7a2390895b56a85f26e8f9b0df46332ad5977c25 Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Thu, 3 Aug 2023 17:09:01 -0400
Subject: [PATCH 07/13] fix spelling

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 .github/wordlist.txt                                | 8 +++-----
 docs/tutorials/integrations/kserve-mm/blue-green.md | 4 ++--
 docs/tutorials/integrations/kserve-mm/canary.md     | 4 ++--
 docs/tutorials/integrations/kserve/blue-green.md    | 4 ++--
 docs/tutorials/integrations/kserve/canary.md        | 2 +-
 5 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/.github/wordlist.txt b/.github/wordlist.txt
index 96bd8226..0e0123d7 100644
--- a/.github/wordlist.txt
+++ b/.github/wordlist.txt
@@ -168,9 +168,7 @@ gz
 xvf
 IMG
 mv
-modelName
+appName
 src
-targetEnv
-templateName
-trafficStrategy
-modelVersions
\ No newline at end of file
+appType
+appVersion
diff --git a/docs/tutorials/integrations/kserve-mm/blue-green.md b/docs/tutorials/integrations/kserve-mm/blue-green.md
index c2dc8234..a77226b9 100644
--- a/docs/tutorials/integrations/kserve-mm/blue-green.md
+++ b/docs/tutorials/integrations/kserve-mm/blue-green.md
@@ -14,7 +14,7 @@ In this tutorial, we use the Istio service mesh to distribute inference requests
 
 ???+ "Before you begin"
     1. Ensure that you have the [kubectl CLI](https://kubernetes.io/docs/reference/kubectl/).
-    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, change your default namespece to `modelmesh-serving`: 
+    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, change your default namespace to `modelmesh-serving`: 
     ```shell
     kubectl config set-context --current --namespace=modelmesh-serving
     ```
@@ -79,7 +79,7 @@ strategy: blue-green
 EOF
 ```
 
-The `initialize` action (with strategy `blue-green`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It futher defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy splits requests 50-50 between the primary and candidate versions. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
+The `initialize` action (with strategy `blue-green`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy splits requests 50-50 between the primary and candidate versions. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
 
 ## Verify routing
 
diff --git a/docs/tutorials/integrations/kserve-mm/canary.md b/docs/tutorials/integrations/kserve-mm/canary.md
index fdf00f3a..8e939159 100644
--- a/docs/tutorials/integrations/kserve-mm/canary.md
+++ b/docs/tutorials/integrations/kserve-mm/canary.md
@@ -14,7 +14,7 @@ In this tutorial, we use the Istio service mesh to distribute inference requests
 
 ???+ "Before you begin"
     1. Ensure that you have the [kubectl CLI](https://kubernetes.io/docs/reference/kubectl/).
-    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, change your default namespece to `modelmesh-serving`: 
+    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, change your default namespace to `modelmesh-serving`: 
     ```shell
     kubectl config set-context --current --namespace=modelmesh-serving
     ```
@@ -79,7 +79,7 @@ strategy: canary
 EOF
 ```
 
-The `initialize` action (with strategy `canary`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It futher defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy sends requests with the header `traffic` set to the value `test` to the candidate version and all remaining requests to the primary version. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
+The `initialize` action (with strategy `canary`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy sends requests with the header `traffic` set to the value `test` to the candidate version and all remaining requests to the primary version. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
 
 ## Verify routing
 
diff --git a/docs/tutorials/integrations/kserve/blue-green.md b/docs/tutorials/integrations/kserve/blue-green.md
index 6f6a207b..8c53673d 100644
--- a/docs/tutorials/integrations/kserve/blue-green.md
+++ b/docs/tutorials/integrations/kserve/blue-green.md
@@ -76,7 +76,7 @@ strategy: blue-green
 EOF
 ```
 
-The `initialize` action (with strategy `blue-green`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It futher defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy splits requests 50-50 between the primary and candidate versions. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
+The `initialize` action (with strategy `blue-green`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy splits requests 50-50 between the primary and candidate versions. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
 
 ## Verify routing
 
@@ -228,7 +228,7 @@ Inspect the `VirtualService` to see that the it has been automatically reconfigu
 
 ## Cleanup
 
-If not alredy deleted, delete the candidate:
+If not already deleted, delete the candidate:
 
 ```shell
 kubectl delete isvc/wisdom-1
diff --git a/docs/tutorials/integrations/kserve/canary.md b/docs/tutorials/integrations/kserve/canary.md
index 0cefeb65..e39953fb 100644
--- a/docs/tutorials/integrations/kserve/canary.md
+++ b/docs/tutorials/integrations/kserve/canary.md
@@ -76,7 +76,7 @@ strategy: canary
 EOF
 ```
 
-The `initialize` action (with strategy `canary`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It futher defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy sends requests with the header `traffic` set to the value `test` to the candidate version and all remaining requests to the primary version. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
+The `initialize` action (with strategy `canary`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy sends requests with the header `traffic` set to the value `test` to the candidate version and all remaining requests to the primary version. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
 
 ## Verify routing
 

From 42f5b3a363846ddb16d88dcb670efd0666f8f287 Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Thu, 3 Aug 2023 17:11:25 -0400
Subject: [PATCH 08/13] fix spelling

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 .github/wordlist.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/wordlist.txt b/.github/wordlist.txt
index 0e0123d7..db0f7393 100644
--- a/.github/wordlist.txt
+++ b/.github/wordlist.txt
@@ -171,4 +171,4 @@ mv
 appName
 src
 appType
-appVersion
+appVersions

From 769e43f04af99f096dc0ede40fcc9f5b12054cf9 Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Fri, 4 Aug 2023 10:21:05 -0400
Subject: [PATCH 09/13] use chart repo

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 docs/tutorials/integrations/kserve-mm/blue-green.md | 9 +++------
 docs/tutorials/integrations/kserve-mm/canary.md     | 6 ++----
 docs/tutorials/integrations/kserve/blue-green.md    | 9 +++------
 docs/tutorials/integrations/kserve/canary.md        | 6 ++----
 4 files changed, 10 insertions(+), 20 deletions(-)

diff --git a/docs/tutorials/integrations/kserve-mm/blue-green.md b/docs/tutorials/integrations/kserve-mm/blue-green.md
index a77226b9..b8fa9072 100644
--- a/docs/tutorials/integrations/kserve-mm/blue-green.md
+++ b/docs/tutorials/integrations/kserve-mm/blue-green.md
@@ -70,8 +70,7 @@ kubectl get inferenceservice wisdom-0
 Initialize model rollout with a blue-green traffic pattern as follows:
 
 ```shell
-#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl apply -f -
+cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
 appType: kserve-modelmesh
 appName: wisdom
 action: initialize
@@ -177,8 +176,7 @@ You can send additional inference requests as described above. They will be hand
 You can modify the weight distribution of inference requests as follows:
 
 ```shell
-#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl apply -f -
+cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
 appType: kserve-modelmesh
 appName: wisdom
 action: modify-weights
@@ -247,8 +245,7 @@ kubectl delete isvc/wisdom-1
 Delete routing:
 
 ```shell
-#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl delete -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl delete -f -
+cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl delete -f -
 appType: kserve-modelmesh
 appName: wisdom
 action: initialize
diff --git a/docs/tutorials/integrations/kserve-mm/canary.md b/docs/tutorials/integrations/kserve-mm/canary.md
index 8e939159..84ae79c6 100644
--- a/docs/tutorials/integrations/kserve-mm/canary.md
+++ b/docs/tutorials/integrations/kserve-mm/canary.md
@@ -70,8 +70,7 @@ kubectl get inferenceservice wisdom-0
 Initialize the routing resources for the application to use a canary rollout strategy:
 
 ```shell
-#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl apply -f -
+cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
 appType: kserve-modelmesh
 appName: wisdom
 action: initialize
@@ -240,8 +239,7 @@ kubectl delete isvc/wisdom-1
 Delete routing:
 
 ```shell
-#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl delete -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl delete -f -
+cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl delete -f -
 appType: kserve-modelmesh
 appName: wisdom
 action: initialize
diff --git a/docs/tutorials/integrations/kserve/blue-green.md b/docs/tutorials/integrations/kserve/blue-green.md
index 8c53673d..53b68c66 100644
--- a/docs/tutorials/integrations/kserve/blue-green.md
+++ b/docs/tutorials/integrations/kserve/blue-green.md
@@ -67,8 +67,7 @@ kubectl get inferenceservice wisdom-0
 Initialize the routing resources for the application to use a blue-green rollout strategy:
 
 ```shell
-#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl apply -f -
+cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
 appType: kserve
 appName: wisdom
 action: initialize
@@ -168,8 +167,7 @@ You can send additional inference requests as described above. They will be hand
 You can modify the weight distribution of inference requests as follows:
 
 ```shell
-#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl apply -f -
+cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
 appType: kserve
 appName: wisdom
 action: modify-weights
@@ -237,8 +235,7 @@ kubectl delete isvc/wisdom-1
 Delete routing:
 
 ```shell
-#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl delete -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl delete -f -
+cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl delete -f -
 appType: kserve
 appName: wisdom
 action: initialize
diff --git a/docs/tutorials/integrations/kserve/canary.md b/docs/tutorials/integrations/kserve/canary.md
index e39953fb..897c289a 100644
--- a/docs/tutorials/integrations/kserve/canary.md
+++ b/docs/tutorials/integrations/kserve/canary.md
@@ -67,8 +67,7 @@ kubectl get inferenceservice wisdom-0
 Initialize the routing resources for the application to use a canary rollout strategy:
 
 ```shell
-#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl apply -f -
+cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl apply -f -
 appType: kserve
 appName: wisdom
 action: initialize
@@ -227,8 +226,7 @@ kubectl delete isvc/wisdom-1
 Delete routing:
 
 ```shell
-#cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl delete -f -
-cat <<EOF | helm template /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/iter8/charts/routing-actions -f - | kubectl delete -f -
+cat <<EOF | helm template routing --repo https://iter8-tools.github.io/iter8 routing-actions -f - | kubectl delete -f -
 appType: kserve
 appName: wisdom
 action: initialize

From 839625423bdac51a3e0e4307dfc5fe59a39f55d9 Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Fri, 4 Aug 2023 10:33:58 -0400
Subject: [PATCH 10/13] fix links to values file

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 docs/tutorials/integrations/kserve-mm/blue-green.md | 2 +-
 docs/tutorials/integrations/kserve-mm/canary.md     | 2 +-
 docs/tutorials/integrations/kserve/blue-green.md    | 2 +-
 docs/tutorials/integrations/kserve/canary.md        | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/tutorials/integrations/kserve-mm/blue-green.md b/docs/tutorials/integrations/kserve-mm/blue-green.md
index b8fa9072..f27c3adb 100644
--- a/docs/tutorials/integrations/kserve-mm/blue-green.md
+++ b/docs/tutorials/integrations/kserve-mm/blue-green.md
@@ -78,7 +78,7 @@ strategy: blue-green
 EOF
 ```
 
-The `initialize` action (with strategy `blue-green`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy splits requests 50-50 between the primary and candidate versions. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
+The `initialize` action (with strategy `blue-green`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy splits requests 50-50 between the primary and candidate versions. For detailed configuration options, see the [Helm chart](https://github.com/iter8-tools/iter8/blob/v0.15.5/charts/routing-actions/values.yaml).
 
 ## Verify routing
 
diff --git a/docs/tutorials/integrations/kserve-mm/canary.md b/docs/tutorials/integrations/kserve-mm/canary.md
index 84ae79c6..b7b28930 100644
--- a/docs/tutorials/integrations/kserve-mm/canary.md
+++ b/docs/tutorials/integrations/kserve-mm/canary.md
@@ -78,7 +78,7 @@ strategy: canary
 EOF
 ```
 
-The `initialize` action (with strategy `canary`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy sends requests with the header `traffic` set to the value `test` to the candidate version and all remaining requests to the primary version. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
+The `initialize` action (with strategy `canary`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy sends requests with the header `traffic` set to the value `test` to the candidate version and all remaining requests to the primary version. For detailed configuration options, see the [Helm chart](https://github.com/iter8-tools/iter8/blob/v0.15.5/charts/routing-actions/values.yaml).
 
 ## Verify routing
 
diff --git a/docs/tutorials/integrations/kserve/blue-green.md b/docs/tutorials/integrations/kserve/blue-green.md
index 53b68c66..55b21dd2 100644
--- a/docs/tutorials/integrations/kserve/blue-green.md
+++ b/docs/tutorials/integrations/kserve/blue-green.md
@@ -75,7 +75,7 @@ strategy: blue-green
 EOF
 ```
 
-The `initialize` action (with strategy `blue-green`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy splits requests 50-50 between the primary and candidate versions. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
+The `initialize` action (with strategy `blue-green`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy splits requests 50-50 between the primary and candidate versions. For detailed configuration options, see the [Helm chart](https://github.com/iter8-tools/iter8/blob/v0.15.5/charts/routing-actions/values.yaml).
 
 ## Verify routing
 
diff --git a/docs/tutorials/integrations/kserve/canary.md b/docs/tutorials/integrations/kserve/canary.md
index 897c289a..86baf6cc 100644
--- a/docs/tutorials/integrations/kserve/canary.md
+++ b/docs/tutorials/integrations/kserve/canary.md
@@ -75,7 +75,7 @@ strategy: canary
 EOF
 ```
 
-The `initialize` action (with strategy `canary`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy sends requests with the header `traffic` set to the value `test` to the candidate version and all remaining requests to the primary version. For detailed configuration options, see the [Helm chart](https://github.com/kalantar/iter8/blob/v0.15/charts/routing-actions/values.yaml).
+The `initialize` action (with strategy `canary`) configures the (Istio) service mesh to route all requests to the primary version of the application (`wisdom-0`). It further defines the routing policy that will be used when changes are observed in the application resources. By default, this routing policy sends requests with the header `traffic` set to the value `test` to the candidate version and all remaining requests to the primary version. For detailed configuration options, see the [Helm chart](https://github.com/iter8-tools/iter8/blob/v0.15.5/charts/routing-actions/values.yaml).
 
 ## Verify routing
 

From b5da3db035138bbfd784d43247d5734cf2227be4 Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Fri, 4 Aug 2023 11:49:45 -0400
Subject: [PATCH 11/13] change version in install commands

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 docs/getting-started/install.md          | 2 +-
 docs/getting-started/installghaction.md  | 7 -------
 docs/getting-started/installgoinstall.md | 5 -----
 docs/tutorials/integrations/ghactions.md | 4 ++--
 4 files changed, 3 insertions(+), 15 deletions(-)
 delete mode 100644 docs/getting-started/installghaction.md
 delete mode 100644 docs/getting-started/installgoinstall.md

diff --git a/docs/getting-started/install.md b/docs/getting-started/install.md
index 1d4fed2a..44e096e0 100644
--- a/docs/getting-started/install.md
+++ b/docs/getting-started/install.md
@@ -1,5 +1,5 @@
 Install the latest stable release of the Iter8 CLI as follows.
 
 ```shell
-go install github.com/iter8-tools/iter8@v0.14
+go install github.com/iter8-tools/iter8@v0.15
 ```
\ No newline at end of file
diff --git a/docs/getting-started/installghaction.md b/docs/getting-started/installghaction.md
deleted file mode 100644
index 0415da4c..00000000
--- a/docs/getting-started/installghaction.md
+++ /dev/null
@@ -1,7 +0,0 @@
-=== "GitHub Actions"
-    Install the latest stable release of the Iter8 CLI in your GitHub Actions workflow as follows.
-
-    ```yaml
-    - name: Install Iter8
-      run: GOBIN=/usr/local/bin go install github.com/iter8-tools/iter8@v0.14
-    ```
diff --git a/docs/getting-started/installgoinstall.md b/docs/getting-started/installgoinstall.md
deleted file mode 100644
index 2fcaca69..00000000
--- a/docs/getting-started/installgoinstall.md
+++ /dev/null
@@ -1,5 +0,0 @@
-Install the latest stable release of the Iter8 CLI as follows.
-
-```yaml
-GOBIN=/usr/local/bin go install github.com/iter8-tools/iter8@v0.14
-```
\ No newline at end of file
diff --git a/docs/tutorials/integrations/ghactions.md b/docs/tutorials/integrations/ghactions.md
index 3ffcaa32..a88e333a 100644
--- a/docs/tutorials/integrations/ghactions.md
+++ b/docs/tutorials/integrations/ghactions.md
@@ -8,11 +8,11 @@ There are two ways that you can use Iter8 with GitHub Actions. You can [run Iter
 
 # Use Iter8 in a GitHub Actions workflow
 
-Install the latest version of the Iter8 CLI using `iter8-tools/iter8@v0.14`. Once installed, the Iter8 CLI can be used as documented in various tutorials. For example:
+Install the latest version of the Iter8 CLI using `iter8-tools/iter8@v0.15`. Once installed, the Iter8 CLI can be used as documented in various tutorials. For example:
 
 ```yaml linenums="1"
 - name: Install Iter8
-  run: GOBIN=/usr/local/bin go install github.com/iter8-tools/iter8@v0.14
+  run: GOBIN=/usr/local/bin go install github.com/iter8-tools/iter8@v0.15
 
 # Launch an experiment inside Kubernetes
 # This assumes that your Kubernetes cluster is accessible from the GitHub Actions pipeline

From ab7db2f7c9adf14ad8f75b3b418b96716c50703c Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Mon, 7 Aug 2023 14:33:59 -0400
Subject: [PATCH 12/13] update links

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 docs/tutorials/integrations/kserve-mm/blue-green.md | 7 +++----
 docs/tutorials/integrations/kserve-mm/canary.md     | 7 +++----
 docs/tutorials/integrations/kserve/blue-green.md    | 5 ++---
 docs/tutorials/integrations/kserve/canary.md        | 5 ++---
 samples/modelmesh-serving/sleep.sh                  | 3 ---
 5 files changed, 10 insertions(+), 17 deletions(-)

diff --git a/docs/tutorials/integrations/kserve-mm/blue-green.md b/docs/tutorials/integrations/kserve-mm/blue-green.md
index f27c3adb..b284e61c 100644
--- a/docs/tutorials/integrations/kserve-mm/blue-green.md
+++ b/docs/tutorials/integrations/kserve-mm/blue-green.md
@@ -93,8 +93,7 @@ To send inference requests to the model:
 === "From within the cluster"
     1. Create a "sleep" pod in the cluster from which requests can be made:
     ```shell
-    # curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/modelmesh-serving/sleep.sh | sh -
-    source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/modelmesh-serving/sleep.sh
+    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.2/samples/modelmesh-serving/sleep.sh | sh -
     ```
 
     2. exec into the sleep pod:
@@ -117,8 +116,8 @@ To send inference requests to the model:
 
     2. Download the proto file and a sample input:
       ```shell
-      curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.13.18/samples/modelmesh-serving/kserve.proto
-      curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.13.18/samples/modelmesh-serving/grpc_input.json
+      curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/modelmesh-serving/kserve.proto
+      curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/modelmesh-serving/grpc_input.json
       ```
 
     3. Send inference requests:
diff --git a/docs/tutorials/integrations/kserve-mm/canary.md b/docs/tutorials/integrations/kserve-mm/canary.md
index b7b28930..c483c0e8 100644
--- a/docs/tutorials/integrations/kserve-mm/canary.md
+++ b/docs/tutorials/integrations/kserve-mm/canary.md
@@ -93,8 +93,7 @@ To send inference requests to the model:
 === "From within the cluster"
     1. Create a "sleep" pod in the cluster from which requests can be made:
     ```shell
-    # curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/modelmesh-serving/sleep.sh | sh -
-    source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/modelmesh-serving/sleep.sh
+    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.2/samples/modelmesh-serving/sleep.sh | sh -
     ```
 
     2. exec into the sleep pod:
@@ -122,8 +121,8 @@ To send inference requests to the model:
 
     2. Download the proto file and a sample input:
       ```shell
-      curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.13.18/samples/modelmesh-serving/kserve.proto
-      curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.13.18/samples/modelmesh-serving/grpc_input.json
+      curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/modelmesh-serving/kserve.proto
+      curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/modelmesh-serving/grpc_input.json
       ```
 
     3. Send inference requests:
diff --git a/docs/tutorials/integrations/kserve/blue-green.md b/docs/tutorials/integrations/kserve/blue-green.md
index 55b21dd2..24d0488f 100644
--- a/docs/tutorials/integrations/kserve/blue-green.md
+++ b/docs/tutorials/integrations/kserve/blue-green.md
@@ -90,8 +90,7 @@ To send inference requests to the model:
 === "From within the cluster"
     1. Create a "sleep" pod in the cluster from which requests can be made:
     ```shell
-    # curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/kserve-serving/sleep.sh | sh -
-    source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/kserve-serving/sleep.sh
+    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/kserve-serving/sleep.sh | sh -
     ```
 
     2. exec into the sleep pod:
@@ -113,7 +112,7 @@ To send inference requests to the model:
 
     2. Download the sample input:
       ```shell
-      # curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/kserve-serving/input.json
+      curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/kserve-serving/input.json
       ```
 
     3. Send inference requests:
diff --git a/docs/tutorials/integrations/kserve/canary.md b/docs/tutorials/integrations/kserve/canary.md
index 86baf6cc..04c689aa 100644
--- a/docs/tutorials/integrations/kserve/canary.md
+++ b/docs/tutorials/integrations/kserve/canary.md
@@ -90,8 +90,7 @@ To send inference requests to the model:
 === "From within the cluster"
     1. Create a "sleep" pod in the cluster from which requests can be made:
     ```shell
-    # curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/kserve-serving/sleep.sh | sh -
-    source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/kserve-serving/sleep.sh
+    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/kserve-serving/sleep.sh | sh -
     ```
 
     2. exec into the sleep pod:
@@ -118,7 +117,7 @@ To send inference requests to the model:
 
     2. Download the sample input:
       ```shell
-      # curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/kserve-serving/input.json
+      curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.15.1/samples/kserve-serving/input.json
       ```
 
     3. Send inference requests:
diff --git a/samples/modelmesh-serving/sleep.sh b/samples/modelmesh-serving/sleep.sh
index dac7e8c5..c203e792 100644
--- a/samples/modelmesh-serving/sleep.sh
+++ b/samples/modelmesh-serving/sleep.sh
@@ -1,5 +1,3 @@
-<<<<<<< HEAD
-=======
 #!/bin/sh
 
 # To use with RedHat OpenShift Service Mesh, set
@@ -423,4 +421,3 @@ EOF
 
 kubectl apply -f $MANIFEST
 rm -f $MANIFEST
->>>>>>> 0d2649e (update kserver-modelmesh use cases)

From de23bc6b7214475685cb6aabb7068ca49e795087 Mon Sep 17 00:00:00 2001
From: Michael Kalantar <kalantar@us.ibm.com>
Date: Mon, 7 Aug 2023 16:54:24 -0400
Subject: [PATCH 13/13] make link explicit

Signed-off-by: Michael Kalantar <kalantar@us.ibm.com>
---
 docs/tutorials/abn/abn.md | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/docs/tutorials/abn/abn.md b/docs/tutorials/abn/abn.md
index 1b4af2fc..5f31c8e6 100644
--- a/docs/tutorials/abn/abn.md
+++ b/docs/tutorials/abn/abn.md
@@ -96,13 +96,12 @@ In this definition, each version of the application is composed of a `Service` a
 
 ## Generate load
 
-In separate shells, port-forward requests to the frontend component and generate load for multiple users. A [script](https://raw.githubusercontent.com/iter8-tools/docs/main/samples/abn-sample/generate_load.sh) is provided for the latter.
+In separate shells, port-forward requests to the frontend component and generate load for multiple users. A [script](https://raw.githubusercontent.com/iter8-tools/docs/main/samples/abn-sample/generate_load.sh) is provided to do this. To use it:
     ```shell
     kubectl port-forward service/frontend 8090:8090
     ```
     ```shell
     curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.15.0/samples/abn-sample/generate_load.sh | sh -s --
-    # source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/abn-sample/generate_load.sh
     ```
     <!-- # source /Users/kalantar/projects/go.workspace/src/github.com/iter8-tools/docs/samples/abn-sample/generate_load.sh -->
 
@@ -128,7 +127,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana in a browser by going to http://localhost:3000/
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000)
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `Iter8` with: