updated the demo instructions.

Author: ipbabble

content/patterns/mlops-fraud-detection/_index.adoc

@@ -2,11 +2,11 @@
 title: MLOps Fraud Detection 
 date: 2023-11-12
 validated: false
-summary: This pattern demonstrates how Red Hat OpenShift AI and MLFlow can be used together to build an end-to-end MLOps platform. It demonstrates this using a credit card fraud detection use case. 
+summary: This pattern demonstrates how {rh-oai} and MLFlow can be used together to build an end-to-end MLOps platform. It demonstrates this using a credit card fraud detection use case. 
 products:
-- Red Hat OpenShift Container Platform
-- Red Hat OpenShift AI
-- Red Hat Open Data Foundation
+- {rh-ocp}
+- {rh-oai}
+- {rh-ocp-data-first}
 industries:
 - financial services
 aliases: /mlops-fraud-detection/

content/patterns/mlops-fraud-detection/mfd-getting-started.adoc

@@ -9,9 +9,6 @@ aliases: /mlops-fraud-detection/getting-started/
 
 include::modules/mfd-deploying-mfd-pattern.adoc[leveloffset=1]
 
-include::modules/mfd-using-mfd-pattern.adoc[leveloffset=1]
-
 = Next Steps
 
-link:https://groups.google.com/g/hybrid-cloud-patterns[Help & Feedback]
-link:https://github.com/validatedpatterns/mlops-fraud-detection/issues[Report Bugs]
+* link:mfd-running-the-demo[Run the demo].

content/patterns/mlops-fraud-detection/mfd-running-the-demo.adoc

@@ -0,0 +1,16 @@
+---
+title: Running the Fraud Detection Demo
+weight: 10
+aliases: /mlops-fraud-detection/mfd-running-the-demo/
+---
+:toc:
+:imagesdir: /images
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+include::modules/mfd-using-mfd-demo.adoc[leveloffset=1]
+
+= Next Steps
+
+link:https://groups.google.com/g/hybrid-cloud-patterns[Help & Feedback]
+link:https://github.com/validatedpatterns/mlops-fraud-detection/issues[Report Bugs]

modules/comm-attributes.adoc

@@ -3,7 +3,7 @@
 //:experimental:
 //:toc: macro
 //:toc-title:
-//:imagesdir: images
+:imagesdir: ../../images
 //:prewrap!:
 :redhat: Red{nbsp}Hat
 //Solution name
@@ -125,3 +125,8 @@
 :AWS: Amazon Web Services (AWS)
 :GCP: Google Cloud Platform (GCP)
 :Azure: Microsoft Azure
+// Red Hat OpenShift AI
+:rh-oai: Red{nbsp}Hat OpenShift Data Science
+:oai: OpenShift Data Science
+// Icons
+:grid: grid.png

modules/edd-about-emerging-disease-detection.adoc

@@ -24,7 +24,7 @@ To demonstrate the effectiveness of the solution this pattern focuses on the spe
 
 == Technology Highlights:
 * Event-Driven Architecture
-* Data Science on OpenShift
+* {rh-oai}
 * Process Automation
 
 == Solution Discussion and Demonstration

modules/edd-deploying-edd-pattern.adoc

@@ -30,7 +30,7 @@ A number of utilities have been built by the validated patterns team to lower th
 . Fork the link:https://github.com/validatedpatterns/emerging-disease-detection[emerging-disease-detection] repo on GitHub. It is necessary to fork because your fork will be updated as part of the GitOps and DevOps processes.
 . Clone the forked copy of this repository.
 +
-[,sh]
+[source,terminal]
 ----
 git clone git@github.com:<your-username>/emerging-disease-detection.git
 ----
@@ -41,7 +41,7 @@ git clone git@github.com:<your-username>/emerging-disease-detection.git
 +
 You do not want to push credentials to GitHub.
 +
-[,sh]
+[source,terminal]
 ----
 cp values-secret.yaml.template ~/values-secret.yaml
 vi ~/values-secret.yaml
@@ -83,7 +83,7 @@ When you edit the file you can make changes to the various DB and Grafana passwo
 
 . Customize the `values-global.yaml` for your deployment
 +
-[,sh]
+[source,terminal]
 ----
 git checkout -b my-branch
 vi values-global.yaml
@@ -108,7 +108,7 @@ main:
     operatorChannel: gitops-1.9
 ----
 
-[,sh]
+[source,terminal]
 ----
    git add values-global.yaml
    git commit values-global.yaml
@@ -118,21 +118,21 @@ main:
 . You can deploy the pattern using the link:/infrastructure/using-validated-pattern-operator/[validated pattern operator]. If you do use the operator then skip to Validating the Environment below.
 . Preview the changes that will be made to the Helm charts.
 +
-[,sh]
+[source,terminal]
 ----
 ./pattern.sh make show
 ----
 
 . Login to your cluster using oc login or exporting the KUBECONFIG
 +
-[,sh]
+[source,terminal]
 ----
 oc login
 ----
 +
 .or set KUBECONFIG to the path to your `kubeconfig` file. For example
 +
-[,sh]
+[source,terminal]
 ----
 export KUBECONFIG=~/my-ocp-env/auth/kubeconfig
 ----
@@ -161,7 +161,7 @@ Emerging Disease Detection Validated Pattern are:
 
 . Apply the changes to your cluster
 +
-[,sh]
+[source,terminal]
 ----
 ./pattern.sh make install
 ----
@@ -188,7 +188,7 @@ them, however you choose to deploy the pattern.
 Display the fully qualified domain names, and matching login credentials, for
 all ArgoCD instances:
 +
-[,sh]
+[source,terminal]
 ----
 ARGO_CMD=`oc get secrets -A -o jsonpath='{range .items[*]}{"oc get -n "}{.metadata.namespace}{" routes; oc -n "}{.metadata.namespace}{" extract secrets/"}{.metadata.name}{" --to=-\\n"}{end}' | grep gitops-cluster`
 CMD=`echo $ARGO_CMD | sed 's|- oc|-;oc|g'`

modules/mfd-architecture.adoc

@@ -31,10 +31,10 @@ image::mlops-fraud-detection/mfd-reference-architecture.png[link="/images/mlops-
 
 The following technologies are used in this solution:
 
-https://www.redhat.com/en/technologies/cloud-computing/openshift/try-it[Red Hat OpenShift Platform]::
+link:https://www.redhat.com/en/technologies/cloud-computing/openshift/try-it[Red Hat OpenShift Container Platform]::
 An enterprise-ready Kubernetes container platform built for an open hybrid cloud strategy. It provides a consistent application platform to manage hybrid cloud, public cloud, and edge deployments. It delivers a complete application platform for both traditional and cloud-native applications, allowing them to run anywhere. OpenShift has a pre-configured, pre-installed, and self-updating monitoring stack that provides monitoring for core platform components. It also enables the use of external secret management systems, for example, HashiCorp Vault in this case, to securely add secrets into the OpenShift platform.
 
-https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai[Red Hat OpenShift AI]::
+link:https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai[Red Hat OpenShift AI]::
 Red Hat® OpenShift® AI is an AI-focused portfolio that provides tools to train, tune, serve, monitor, and manage AI/ML experiments and models on Red Hat OpenShift. Bring data scientists, developers, and IT together on a unified platform to deliver AI-enabled applications faster.
 
 https://www.redhat.com/en/technologies/cloud-computing/openshift/try-it[Red Hat OpenShift GitOps]::

modules/mfd-deploying-mfd-pattern.adoc

@@ -30,7 +30,7 @@ A number of utilities have been built by the validated patterns team to lower th
 . Fork the link:https://github.com/validatedpatterns/mlops-fraud-detection[mlops-fraud-detection] repo on GitHub. It is necessary to fork because your fork will be updated as part of the GitOps and DevOps processes.
 . Clone the forked copy of this repository.
 +
-[,sh]
+[source,terminal]
 ----
 git clone git@github.com:<your-username>/mlops-fraud-detection.git
 ----
@@ -41,7 +41,7 @@ git clone git@github.com:<your-username>/mlops-fraud-detection.git
 +
 You do not want to push credentials to GitHub.
 +
-[,sh]
+[source,terminal]
 ----
 cp values-secret-mlops-fraud-detection.yaml.template ~/values-secret.yaml
 vi ~/values-secret.yaml
@@ -59,7 +59,7 @@ When you edit the file you can make changes to the various DB and Grafana passwo
 
 . Customize the `values-global.yaml` for your deployment
 +
-[,sh]
+[source,terminal]
 ----
 git checkout -b my-branch
 vi values-global.yaml
@@ -84,7 +84,7 @@ main:
     operatorChannel: gitops-1.9
 ----
 
-[,sh]
+[source,terminal]
 ----
    git add values-global.yaml
    git commit values-global.yaml
@@ -94,21 +94,21 @@ main:
 . You can deploy the pattern using the link:/infrastructure/using-validated-pattern-operator/[validated pattern operator]. If you do use the operator then skip to Validating the Environment below.
 . Preview the changes that will be made to the Helm charts.
 +
-[,sh]
+[source,terminal]
 ----
 ./pattern.sh make show
 ----
 
 . Login to your cluster using oc login or exporting the KUBECONFIG
 +
-[,sh]
+[source,terminal]
 ----
 oc login
 ----
 +
 .or set KUBECONFIG to the path to your `kubeconfig` file. For example
 +
-[,sh]
+[source,terminal]
 ----
 export KUBECONFIG=~/my-ocp-env/auth/kubeconfig
 ----
@@ -137,7 +137,7 @@ MLOps Fraud Detection Validated Pattern are:
 
 . Apply the changes to your cluster
 +
-[,sh]
+[source,terminal]
 ----
 ./pattern.sh make install
 ----
@@ -164,7 +164,7 @@ them, however you choose to deploy the pattern.
 Display the fully qualified domain names, and matching login credentials, for
 all ArgoCD instances:
 +
-[,sh]
+[source,terminal]
 ----
 ARGO_CMD=`oc get secrets -A -o jsonpath='{range .items[*]}{"oc get -n "}{.metadata.namespace}{" routes; oc -n "}{.metadata.namespace}{" extract secrets/"}{.metadata.name}{" --to=-\\n"}{end}' | grep gitops-cluster`
 CMD=`echo $ARGO_CMD | sed 's|- oc|-;oc|g'`