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RedHatQuickCourses · Jun 12, 2024 · 4b35f41 · 4b35f41
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diff --git a/.DS_Store b/.DS_Store
diff --git a/modules/.DS_Store b/modules/.DS_Store
diff --git a/modules/ROOT/pages/index.adoc b/modules/ROOT/pages/index.adoc
@@ -19,11 +19,15 @@ IMPORTANT: The hands-on labs in this course were created and tested with RHOAI v
 
 The PTL team acknowledges the valuable contributions of the following Red Hat associates:
 
-*Christopher Nuland
+* Christopher Nuland
 
-*Vijay Chebolu & Team
+ * Vijay Chebolu
 
-*Karlos Knox
+ * Noel O'Conner
+
+ * Hunter Gerlach
+
+ * Karlos Knox
 
 == Classroom Environment
 
@@ -40,7 +44,7 @@ When ordering this catalog item in RHDP:
 
   . Enter Learning RHOAI in the Salesforce ID field
 
-  . Scroll to the bottom, check the box to confirm acceptance of terms and conditions
+  . Scroll to the bottom, and check the box to confirm acceptance of terms and conditions
 
   . Click order
 
@@ -56,16 +60,16 @@ For Red Hat partners who do not have access to RHDP, provision an environment us
 
 The overall objectives of this course include:
 
- * Familiarize utilizing Red Hat OpenShift AI to Serve & Interact with an LLM.
+ * Utilize Red Hat OpenShift AI to serve & interact with an LLM
 
- * Installing Red Hat OpenShift AI Operators & Dependencies
+ * Install Red Hat OpenShift AI operators & dependencies
 
- * Add a custom Model Serving Runtime
+ * Add a custom model serving runtime
 
  * Create a data science project, workbench & data connections
 
- * Load an LLM model into the Ollama Runtime Framework
+ * Load an LLM model into the Ollama runtime framework
 
- * Import (from Git repositories), interact with LLM model via a Jupyter Notebook
+ * Import (from git repositories), interact with LLM model via a Jupyter Notebooks
 
  * Experiment with the Mistral LLM
diff --git a/modules/chapter2/images/createDSC.png b/modules/chapter2/images/createDSC.png
diff --git a/modules/chapter2/images/createsecret.png b/modules/chapter2/images/createsecret.png
diff --git a/modules/chapter2/images/dcsyamlfile.png b/modules/chapter2/images/dcsyamlfile.png
diff --git a/modules/chapter2/images/openshiftai_operator.png b/modules/chapter2/images/openshiftai_operator.png
diff --git a/modules/chapter2/images/openshiftingress_project.png b/modules/chapter2/images/openshiftingress_project.png
diff --git a/modules/chapter2/images/serverless_operator.png b/modules/chapter2/images/serverless_operator.png
diff --git a/modules/chapter2/pages/section1.adoc b/modules/chapter2/pages/section1.adoc
@@ -32,7 +32,9 @@ This exercise uses the Red Hat Demo Platform; specifically the OpenShift Contain
 
  Installing these Operators prior to the installation of the OpenShift AI Operator in my experience has made a difference in OpenShift AI acknowledging the availability of these components and adjusting the initial configuration to shift management of these components to OpenShift AI. 
 
-. Navigate to **Operators** -> **OperatorHub** and search for *OpenShift AI*.
+* Navigate to **Operators** -> **OperatorHub** and search for *OpenShift AI*.
+
+image::openshiftai_operator.png[]
 
 . Click on the `Red{nbsp}Hat OpenShift AI` operator. In the pop up window that opens, ensure you select the latest version in the *stable* channel and click on **Install** to open the operator's installation view.
 +

diff --git a/modules/chapter2/pages/section2.adoc b/modules/chapter2/pages/section2.adoc
@@ -24,6 +24,9 @@ The content of the Secret (data) should contain two items, *tls.cert* and *tls.k
     . In the Navigation pane on the left, click on the *Workloads* section, then *Secrets* under Workloads.
     . From the Project dropdown, toggle the *show default projects* radial button to on. 
     . Select the *openshift-ingress* project from the list. 
+
+image::openshiftingress_project.png[]
+
     . Locate the file named *ingress-certs-(XX-XX-2024)*, type should be *Opaque*
     . Click on the filename to open the secret, Select the *YAML Tab*
     . Copy all the text from the window, insure you scroll down.  (CTL-A should work).
@@ -48,8 +51,10 @@ tls.key: >-
     LS0tLS1CRUd...
 type: kubernetes.io/tls
 ```
+image::createsecret.png[]
 
-* Copy the Name in red portion of the text  (optional, but helpful)
+
+* Copy the name of the secret from line 4, just the name  (optional, but helpful)
 * Click *create* to apply this YAML into the istio-system proejct (namespace).
 
 *We have copied the Secret used by OCP & made it available be used by OAI.*
@@ -81,12 +86,15 @@ serving:
     managementState: Managed
     name: knative-serving
 ```
+image::dcsyamlfile.png[]
 
 Once you have made those changes to the YAML file, *Click Create* to Deploy the Data Science Cluster.  
 
+image::createDSC.png[]
+
 Single Model Serve Platform will now be deployed / expose ingress connections with the same certificate as OpenShift Routes. Endpoints will be accessible using TLS without having to ignore error messages or create special configurations.
 
-== Epilogue
+== OpenShift AI install summary
 
 Congradulations, you have successful completed the installation of OpenShift AI on an OpenShift Container Cluster. OpenShift AI is now running as new Dashboard!