aws-samples
diff --git a/‎content/020_prerequisites/k8stools.md
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diff --git a/‎content/030_eksctl/launcheks.md
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diff --git a/‎content/advanced/310_servicemesh_with_istio/download.md
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diff --git a/‎content/advanced/310_servicemesh_with_istio/introduction.md
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diff --git a/‎content/advanced/410_batch/artifact.md
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diff --git a/‎content/advanced/410_batch/deploy.md
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diff --git a/‎content/advanced/410_batch/workflow-simple.md
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diff --git a/‎content/advanced/420_kubeflow/cleanup.md
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diff --git a/‎content/advanced/420_kubeflow/dashboard.md
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diff --git a/‎content/advanced/420_kubeflow/fairing.md
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@@ -17,7 +17,7 @@ for the download links.](https://docs.aws.amazon.com/eks/latest/userguide/gettin
 
 ```bash
 sudo curl --silent --location -o /usr/local/bin/kubectl \
-   https://amazon-eks.s3.us-west-2.amazonaws.com/1.19.6/2021-01-05/bin/linux/amd64/kubectl
+   https://s3.us-west-2.amazonaws.com/amazon-eks/1.21.5/2022-01-21/bin/linux/amd64/kubectl
 
 sudo chmod +x /usr/local/bin/kubectl
 ```
 
@@ -32,7 +32,7 @@ If you do see the correct role, proceed to next step to create an EKS cluster.
 ### Create an EKS cluster
 
 {{% notice warning %}}
-`eksctl` version must be 0.38.0 or above to deploy EKS 1.19, [click here](/030_eksctl/prerequisites) to get the latest version.
+`eksctl` version must be 0.58.0 or above to deploy EKS 1.21, [click here](/030_eksctl/prerequisites) to get the latest version.
 {{% /notice %}}
 
 Create an eksctl deployment file (eksworkshop.yaml) use in creating your cluster using the following syntax:
@@ -46,7 +46,7 @@ kind: ClusterConfig
 metadata:
   name: eksworkshop-eksctl
   region: ${AWS_REGION}
-  version: "1.19"
+  version: "1.21"
 
 availabilityZones: ["${AZS[0]}", "${AZS[1]}", "${AZS[2]}"]
 
 
@@ -8,11 +8,11 @@ draft: false
 Before we can get started configuring Istio we’ll need to first install the command line tools that you will interact with. To do this run the following.
 
 {{% notice info %}}
-We will use istio version `1.10.0`
+We will use istio version `1.14.1`
 {{% /notice %}}
 
 ```bash
-echo 'export ISTIO_VERSION="1.10.0"' >> ${HOME}/.bash_profile
+echo 'export ISTIO_VERSION="1.14.1"' >> ${HOME}/.bash_profile
 source ${HOME}/.bash_profile
 ```
 
 
@@ -6,7 +6,7 @@ draft: false
 ---
 
 {{% notice info %}}
-This chapter has been updated to Istio 1.10.x
+This chapter has been updated to Istio 1.14.x
 [Click here](https://istio.io/latest/news/releases/1.10.x/) to know more about this new release.
 {{% /notice %}}
 
 
@@ -88,7 +88,7 @@ If `ROLE_NAME` is not set, please review: [/030_eksctl/test/](/030_eksctl/test/)
 ROLE_NAME is eksctl-eksworkshop-eksctl-nodegro-NodeInstanceRole-RPDET0Z4IJIF
 {{< /output >}}
 
-Create and policy and attach to the worker node role.
+Create a policy and attach to the worker node role.
 
 ```bash
 cat <<EoF > ~/environment/batch_policy/k8s-s3-policy.json
 
@@ -7,7 +7,7 @@ draft: false
 
 ### Deploy Argo Controller
 
-Argo run in its own namespace and deploys as a CustomResourceDefinition.
+Argo runs in its own namespace and deploys as a CustomResourceDefinition (CRD).
 
 Deploy the Controller and UI.
 
 
@@ -29,7 +29,7 @@ EoF
 Now deploy the workflow using the argo CLI.
 
 {{% notice note %}}
-You can also run workflow specs directly using kubectl but the argo CLI provides syntax checking, nicer output, and requires less typing. For the equivalent `kubectl` commands, see [Argo CLI](https://argoproj.github.io/docs/argo/examples/readme.html#argo-cli).
+You can also run workflow specs directly using kubectl but the argo CLI provides syntax checking, nicer output, and requires less typing. For the equivalent `kubectl` commands, see [Argo CLI](https://argoproj.github.io/argo-workflows/walk-through/argo-cli/#argo-cli).
 {{% /notice %}}
 
 ```bash
 
@@ -23,11 +23,21 @@ aws s3 rb s3://$S3_BUCKET --force --region $AWS_REGION
 kubectl delete secret/aws-secret
 kubectl delete secret/aws-secret -n kubeflow
 ```
-Run these commands to uninstall Kubeflow from your EKS cluster
+Next, delete all existing Kubeflow profiles. 
+
+```bash
+kubectl get profile
+kubectl delete profile --all
 ```
-cd ${KF_DIR}
-kfctl delete -V -f ${CONFIG_FILE}
+
+You can delete a Kubeflow deployment by running the `kubectl delete` command on the manifest according to the deployment option you chose. For example, to delete a vanilla installation, run the following command:
+
+```bash
+kustomize build deployments/vanilla/ | kubectl delete -f -
 ```
+
+This command assumes that you have the repository in the same state as when you installed Kubeflow.
+
 Scale the cluster back to previous size
 ```
 eksctl scale nodegroup --cluster eksworkshop-eksctl --name $NODEGROUP_NAME --nodes 3
 
@@ -1,6 +1,6 @@
 ---
 title: "Kubeflow Dashboard"
-date: 2019-08-22T00:00:00-08:00
+date: 2022-07-16T00:00:00-08:00
 weight: 20
 draft: false
 ---
@@ -18,12 +18,10 @@ Leave the current terminal running because if you kill the process, you will loo
 
 ![dashboard](/images/kubeflow/dashboard-welcome.png)
 
-Click on **Start Setup**
+In the login screen, use the default email (`[email protected]`) and password (`12341234`)
 
-Specify the namespace as **eksworkshop**
+![dashboard](/images/kubeflow/dashboard-login.png)
 
-![dashboard](/images/kubeflow/dashboard-create-namespace.png)
-
-Click on **Finish** to view the dashboard
+After logging in, you will be redirected to the Kubeflow dashboard screen:
 
 ![dashboard](/images/kubeflow/dashboard-first-look.png)
@@ -1,6 +1,6 @@
 ---
 title: "Fairing"
-date: 2019-11-13T21:55:32-05:00
+date: 2022-07-16T00:00:00-08:00
 weight: 60
 pre: '<i class="fa fa-film" aria-hidden="true"></i> '
 draft: false
@@ -36,6 +36,8 @@ Create new Python 3 Notebook if one doesn't exist. Run the command below to clon
 ```
 Click Run. This will clone the repo into your notebook server
 
+![dashboard](/images/kubeflow/fairing-git-clone.png)
+
 Close the notebook tab, go back to the notebook server, select the notebook that we just used and click **Shutdown**.
 
 ![dashboard](/images/kubeflow/fairing-shutdown-notebook.png)
@@ -54,6 +56,7 @@ Review the content and click first cell and click **Run**. This will let you ins
 
 Wait till it finishes, go to next cell and click **Run**. Here is expected result
 ![dashboard](/images/kubeflow/fairing-install-from-github.png)
+![dashboard](/images/kubeflow/fairing-validate-installed-packages.png)
 
 Now that we have fairing installed, we will train a model authored in Python. The model will create a linear regression model that allows us to learn a function or relationship from a given set of continuous data. For example, we are given some data points of x and corresponding y and we need to learn the relationship between them that is called a hypothesis.
 
@@ -66,9 +69,13 @@ Now, lets use fairing and push the image into ECR which can be used for remote t
 
 Before you run authenticate with ECR, change the region if needed. Run this cell and login so that you can perform ECR operations
 
+![dashboard](/images/kubeflow/fairing-ecr-login.png)
+
 Run the next cell and create an ECR repository (fairing-job) in the same region. You should see similar output
+
 ![dashboard](/images/kubeflow/fairing-create-ecr-repo.png)
 
+
 Let's run next cell. Fairing pushes the image to ECR and then deploys the model remotely
 
 ![dashboard](/images/kubeflow/fairing-remote-job.png)