create modules for getting started

content/patterns/medical-diagnosis/getting-started.adoc

@@ -9,20 +9,26 @@ aliases: /medical-diagnosis/getting-started/
 :_content-type: ASSEMBLY
 include::modules/comm-attributes.adoc[]
 
-== Prerequisites
-
-. An OpenShift cluster (Go to https://console.redhat.com/openshift/create[the OpenShift console]). Cluster must have a dynamic StorageClass to provision PersistentVolumes. See also link:../../medical-diagnosis/cluster-sizing[sizing your cluster].
-. A GitHub account (and a token for it with repositories permissions, to read from and write to your forks)
-. S3-capable Storage set up in your public/private cloud for the x-ray images
-. The helm binary, see link:https://helm.sh/docs/intro/install/[here]
-
+//Module to be included
+//:_content-type: PROCEDURE
+//:imagesdir: ../../../images
+[id="deploying-med-pattern"]
+= Deploying the {med-pattern}
+
+//Note that Block titles like these don't render correctly on the site
+.Prerequisites
+
+* An OpenShift cluster
+ ** To create an OpenShift cluster, go to the https://console.redhat.com/[Red Hat Hybrid Cloud console].
+ ** Select *OpenShift \-> Clusters \-> Create cluster*.
+ ** The cluster must have a dynamic `StorageClass` to provision `PersistentVolumes`. See link:../../medical-diagnosis/cluster-sizing[sizing your cluster].
+* A GitHub account and a token for it with repositories permissions, to read from and write to your forks.
+. S3-capable Storage set up in your public or private cloud for the x-ray images
+. The Helm binary, see link:https://helm.sh/docs/intro/install/[Installing Helm]
 For installation tooling dependencies, see link:https://hybrid-cloud-patterns.io/learn/quickstart/[Patterns quick start].
 
-The use of this pattern depends on having a Red Hat OpenShift cluster. In this version of the validated pattern
-there is no dedicated Hub / Edge cluster for the *Medical Diagnosis* pattern.
+The use of this pattern depends on having at least one running OpenShift cluster. There is no dedicated hub or edge cluster the {med-pattern}.
 
-If you do not have a running Red Hat OpenShift cluster you can start one on a
-public or private cloud by using link:https://console.redhat.com/openshift/create[Red Hat's cloud service].
 
 [id="setting-up-an-s3-bucket-for-the-xray-images-getting-started"]
 === Setting up an S3 Bucket for the xray-images
@@ -54,7 +60,7 @@ python s3-create.py -b mytest-bucket -r us-west-2 -p
 python s3-sync-buckets.py -s com.validated-patterns.xray-source -t mytest-bucket -r us-west-2
 ----
 
-The output should look similar to this edited/compressed output.
+.Example output
 
 image:/videos/bucket-setup.svg[Bucket setup]
 
@@ -168,11 +174,11 @@ $ export KUBECONFIG=~/my-ocp-env/auth/kubeconfig
 == Check the values files before deployment
 
 You can run a check before deployment to make sure that you have the required variables to deploy the
-Medical Diagnosis Validated Pattern.
+{med-pattern}.
 
 You can run `make predeploy` to check your values. This will allow you to review your values and changed them in
 the case there are typos or old values.  The values files that should be reviewed prior to deploying the
-Medical Diagnosis Validated Pattern are:
+{med-pattern} are:
 
 |===
 | Values File | Description

content/patterns/medical-diagnosis/ideas-for-customization.adoc

@@ -22,11 +22,11 @@ The {med-pattern} can answer the call to either of these requirements by using
 == Understanding different ways to use the {med-pattern}
 
 . The {med-pattern} is scanning X-Ray images to determine the probability that a patient might or might not have Pneumonia. Continuing with the medical path, the pattern could be used for other early detection scenarios that use object detection and classification. For example, the pattern could be used to scan C/T images for anomalies in the body such as Sepsis, Cancer, or even benign tumors. Additionally, the pattern could be used for detecting blood clots, some heart disease, and bowel disorders like Crohn's disease.
-. The Transportation Security Agency (TSA) could use the {med-pattern} in a way that enhances their existing scanning capabilities to detect with a higher probability restricted items carried on a person or hidden away in a piece of luggage. With MLOps the model is constantly training and learning to better detect those items that are dangerous but aren't necessarily metallic such as a firearm or knife. The model is also training to dismiss those items that are authorized ultimately saving us from being stopped and searched at security checkpoints!
-. Militaries could use images collected from drones, satellites or other platforms to identify objects and determine with probability what that object is. For example, the model could be trained to determine a type of ship, potentially its country of origin and other identifying characteristics.
+. The Transportation Security Agency (TSA) could use the {med-pattern} in a way that enhances their existing scanning capabilities to detect with a higher probability restricted items carried on a person or hidden away in a piece of luggage. With Machine Learning Operations (MLOps), the model is constantly training and learning to better detect those items that are dangerous but which are not necessarily metallic, such as a firearm or a knife. The model is also training to dismiss those items that are authorized; ultimately saving passengers from being stopped and searched at security checkpoints.
+. Militaries could use images collected from drones, satellites, or other platforms to identify objects and determine with probability what that object is. For example, the model could be trained to determine a type of ship, potentially its country of origin, and other such identifying characteristics.
 . Manufacturing companies could use the pattern to inspect finished products as they roll off a production line. An image of the item, including using different types of light, could be analyzed to help expose defects before packaging and distributing. The item could be routed to a defect area.
 
-These are just a few ideas to help get the creative juices flowing for how you could use the medical-diagnosis pattern as a framework for your application.
+These are just a few ideas to help you understand how you could use the {med-pattern} as a framework for your application.
 
 //We have relevant links on the patterns page
 //AI: Why does this point to AEG though? https://github.com/hybrid-cloud-patterns/ansible-edge-gitops/issues[Report Bugs]