product name change and typos

README.md

@@ -1,6 +1,6 @@
 # Deploying Machine Learning Models with Red Hat OpenShift Data Science
 
-This course is the fifth in a series of 6 courses about Red Hat OpenShift Data Science (RHODS). This course teaches you how to deploy machine learning models in RHODS, and how to consume them from client applications.
+This course is the fourth in a series of five courses about Red Hat OpenShift AI (RHOAI). This course teaches you how to deploy machine learning models in RHOAI, and how to consume them from client applications.
 
 # Creating Course Content
 

antora-playbook.yml

@@ -1,5 +1,5 @@
 site:
-  title: Deploying Machine Learning Models with Red Hat OpenShift Data Science
+  title: Deploying Machine Learning Models with Red Hat OpenShift AI
   start_page: rhods-deploy::index.adoc
 
 content:

antora.yml

@@ -1,5 +1,5 @@
 name: rhods-deploy
-title: Deploying Machine Learning Models with Red Hat OpenShift Data Science
+title: Deploying Machine Learning Models with Red Hat OpenShift AI
 version: 1.33
 nav:
 - modules/ROOT/nav.adoc

modules/ROOT/pages/index.adoc

@@ -1,15 +1,14 @@
-= Deploying Machine Learning Models with Red Hat OpenShift Data Science
+= Deploying Machine Learning Models with Red Hat OpenShift AI
 :navtitle: Home
 
-Welcome to this quick course on the _Deploying Machine Learning Models with Red Hat OpenShift Data Science_.
-This course is the fifth in a series of 6 courses about Red Hat OpenShift Data Science:
+Welcome to this quick course on the _Deploying Machine Learning Models with Red Hat OpenShift AI_.
+This course is the *fourth* in a series of *five* courses about Red Hat OpenShift AI:
 
-1. https://redhatquickcourses.github.io/rhods-intro[Introduction to Red Hat OpenShift Data Science]
-2. https://redhatquickcourses.github.io/rhods-admin[Red Hat OpenShift Data Science Administration]
-3. Data Analysis and Visualization with Red Hat OpenShift Data Science (Under Development)
-4. https://redhatquickcourses.github.io/rhods-model[Creating Machine Learning Models with Red Hat OpenShift Data Science]
-5. Deploying Machine Learning Models with Red Hat OpenShift Data Science (_This course_)
-6. https://redhatquickcourses.github.io/rhods-pipelines[Automation using Data Science Pipelines]
+1. https://redhatquickcourses.github.io/rhods-intro[Introduction to Red Hat OpenShift AI]
+2. https://redhatquickcourses.github.io/rhods-admin[Red Hat OpenShift AI Administration]
+3. https://redhatquickcourses.github.io/rhods-model[Creating Machine Learning Models with Red Hat OpenShift Data Science]
+4. Deploying Machine Learning Models with Red Hat OpenShift AI (_This course_)
+5. https://redhatquickcourses.github.io/rhods-pipelines[Automation using Data Science Pipelines]
 
 NOTE: After you have completed all the courses in the learning path, you can attempt the https://github.com/RedHatQuickCourses/rhods-qc-apps/tree/main/7.hands-on-lab["Hit the RHODS"] exercise, that tests your understanding of the concepts taught in all the courses.
 
@@ -24,24 +23,24 @@ The PTL team acknowledges the valuable contributions of the following Red Hat as
 
 == Classroom Environment
 
-There are two options, based on whether you are taking this course standalone (just this course), or as part of the full 6 course learning path where you installed RHODS on an OpenShift cluster in the second course in the learning path, _Red Hat OpenShift Data Science Administration_.
+There are two options, based on whether you are taking this course standalone (just this course), or as part of the full five course learning path where you installed RHOAI on an OpenShift cluster in the second course in the learning path, _Red Hat OpenShift AI Administration_.
 
-=== Option 1: Standalone (RHODS Pre-installed on OpenShift)
+=== Option 1: Standalone (RHOAI Pre-installed on OpenShift)
 
 You will use the https://demo.redhat.com/catalog?search=openshift+data+science&item=babylon-catalog-prod%2Fsandboxes-gpte.ocp4-workshop-rhods-base-aws.prod[Base RHODS on AWS] catalog item in the Red Hat Demo Platform (RHDP) to run the hands on exercises in this course.
 
 This classroom has a pre-installed version of Red Hat OpenShift Data Science on OpenShift. 
 
-=== Option 2: Six Course Learning Path
+=== Option 2: Five Course Learning Path
 
-Continue using the https://demo.redhat.com/catalog?search=Red+Hat+OpenShift+Container+Platform+4.13+Workshop&item=babylon-catalog-prod%2Fopenshift-cnv.ocp413-wksp-cnv.prod[Red Hat OpenShift Container Platform 4.13 Workshop] catalog item from the _Red Hat OpenShift Data Science Administration_ course.
+Continue using the https://demo.redhat.com/catalog?search=Red+Hat+OpenShift+Container+Platform+4.13+Workshop&item=babylon-catalog-prod%2Fopenshift-cnv.ocp413-wksp-cnv.prod[Red Hat OpenShift Container Platform 4.13 Workshop] catalog item from the _Red Hat OpenShift AI Administration_ course.
 
 [TIP]
 ====
 To prevent problems when allocating the workbench pods, make sure that your catalog item has been configured with `64Gi` as the worker memory size.
 ====
 
-This classroom does *NOT* have RHODS pre-installed. You are expected to complete the _Red Hat OpenShift Data Science Administration_ course, where you install and configure a basic RHODS instance, and then continue with this course.
+This classroom does *NOT* have RHOAI pre-installed. You are expected to complete the _Red Hat OpenShift AI Administration_ course, where you install and configure a basic RHOAI instance, and then continue with this course.
 
 == Prerequisites
 
@@ -56,7 +55,7 @@ This classroom does *NOT* have RHODS pre-installed. You are expected to complete
 The overall objectives of this course include:
 
 * Deploy and serve machine learning models on OpenShift
-* Model deployment with RHODS model serving
+* Model deployment with RHOAI model serving
 * Model serving authentication and authorization
 * Troubleshoot deployed models
 * Send inference requests to deployed models via REST API calls

modules/chapter1/pages/index.adoc

@@ -1,4 +1,4 @@
-= Model Serving in RHODS
+= Model Serving in Red Hat OpenShift AI
 
 == Introduction
 

modules/chapter1/pages/section1.adoc

@@ -61,7 +61,7 @@ When deploying machine learning models, we need to deploy a container that serve
 
 ==== Train a model
 
-Using a RHODS instance, let us train and deploy an example.
+Using a RHOAI instance, let us train and deploy an example.
 
 . In a data science project, create a `Standard Data Science`workbench.
 Then, open the workbench to go to the JupyterLab interface.
@@ -133,7 +133,7 @@ The pickle model that we previously exported can be used in a Flask application.
 
 [IMPORTANT]
 ====
-Although we are actually serving a model with Flask in the exercise, Flask is not considered part of the Model Serving feature. This example represents one way in which some customers decide to embed their models in containers, although RHODS provides for mechanisms that can make this process of serving a model a simpler process, when provided with the proper model formats.
+Although we are actually serving a model with Flask in the exercise, Flask is not considered part of the Model Serving feature. This example represents one way in which some customers decide to embed their models in containers, although RHOAI provides for mechanisms that can make this process of serving a model a simpler process, when provided with the proper model formats.
 ====
 
 . In your computer, create a new directory to save the source code of the web application.
@@ -300,18 +300,18 @@ There are automated and faster ways to perform these steps. In the following sec
 
 === RHOAI Model Serving Runtimes
 
-In the previous example, we manually created a Model Server by sending the model to an image that can interpret the model and expose it for consumtion. In our example we used Flask.
+In the previous example, we manually created a Model Server by sending the model to an image that can interpret the model and expose it for consumption. In our example we used Flask.
 
-However, in OpenShift AI, you do not need to manually create serving runtimes.
-By default, OpenShift AI includes a pre-configured model serving runtime, OpenVINO, which can load, execute, and expose  models trained with TensorFlow and PyTorch.
+However, in Red Hat OpenShift AI, you do not need to manually create serving runtimes.
+By default, Red Hat OpenShift AI includes a pre-configured model serving runtime, OpenVINO, which can load, execute, and expose  models trained with TensorFlow and PyTorch.
 OpenVINO supports various model formats, such as the following ones:
 
 - https://onnx.ai[ONNX]: An open standard for machine learning interoperability.
-- https://docs.openvino.ai/latest/openvino_ir.html[OpenVino IR]: The proprietary model format of OpenVINO, the model serving runtime used in OpenShift Data Science.
+- https://docs.openvino.ai/latest/openvino_ir.html[OpenVino IR]: The proprietary model format of OpenVINO, the model serving runtime used in OpenShift AI.
 
 In order to leverage the benefits of OpenVINO, you must:
 
-. Export the model in a format compatible with one of the available RHODS runtimes.
+. Export the model in a format compatible with one of the available RHOAI runtimes.
 . Upload the model to an S3
 . Create a Data Connection to the S3 containing the model
 . Create or use one of the available serving runtimes in a Model Server configuration that specifies the size and resources to use while setting up an inference engine.

modules/chapter1/pages/section2.adoc

@@ -40,13 +40,13 @@ oc get routes -n object-datastore | grep minio-api | awk '{print $2}'
 +
 [INFO]
 ====
-Use this route as the S3 API endpoint. Basically, this is the URL that we will use when creating a data connection to the S3 in RHODS.
+Use this route as the S3 API endpoint. Basically, this is the URL that we will use when creating a data connection to the S3 in RHOAI.
 ==== 
 
 == Training The Model
 We will use the iris dataset model for this excercise.
 
-. Using a JupyterLab workbench at RHODS, import the repository: https://github.com/RedHatQuickCourses/rhods-qc-apps.git
+. Using a JupyterLab workbench at RHOAI, import the repository: https://github.com/RedHatQuickCourses/rhods-qc-apps.git
 +
 [TIP]
 ====
@@ -83,7 +83,7 @@ Make sure to create a new path in your bucket, and upload to such path, not to r
 
 == Create A Data Connection
 
-. In RHODS dashboard, create a project named **iris-project**.
+. In the RHOAI dashboard, create a project named **iris-project**.
 
 . In the **Data Connections** section, create a Data Connection to your S3
 +

modules/chapter1/pages/section3.adoc

@@ -1,8 +1,8 @@
 = Creating a Custom Model Serving Runtime
 
-A model-serving runtime provides integration with a specified model server and the model frameworks that it supports. By default, Red Hat OpenShift Data Science includes the OpenVINO Model Server runtime. However, if this runtime doesn’t meet your needs (it doesn’t support a particular model framework, for example), you might want to add your own, custom runtimes.
+A model-serving runtime provides integration with a specified model server and the model frameworks that it supports. By default, Red Hat OpenShift AI includes the OpenVINO Model Server runtime. However, if this runtime doesn’t meet your needs (it doesn’t support a particular model framework, for example), you might want to add your own, custom runtimes.
 
-As an administrator, you can use the OpenShift Data Science interface to add and enable custom model-serving runtimes. You can then choose from your enabled runtimes when you create a new model server.
+As an administrator, you can use the OpenShift AI interface to add and enable custom model-serving runtimes. You can then choose from your enabled runtimes when you create a new model server.
 
 == Prerequisite
 
@@ -16,12 +16,12 @@ This exercise will guide you through the broad steps necessary to deploy a custo
 
 [NOTE]
 ====
-While RHODS supports the ability to add your own runtime, it does not support the runtimes themselves. Therefore, it is up to you to configure, adjust and maintain your custom runtimes.
+While RHOAI supports the ability to add your own runtime, it does not support the runtimes themselves. Therefore, it is up to you to configure, adjust and maintain your custom runtimes.
 ====
 
 == Adding The Custom Runtime
 
-. Log in to RHODS with a user who is part of the RHODS admin group
+. Log in to RHOAI with a user who is part of the RHOAI admin group
 
 . Navigate to the Settings menu, then Serving Runtimes
 +