Merge pull request #6 from RedHatQuickCourses/updatesv5

page restructure and lab env final

modules/LABENV/pages/index.adoc

@@ -5,7 +5,7 @@
 We will use the https://demo.redhat.com/catalog?item=babylon-catalog-prod%2Fopenshift-cnv.ocpmulti-wksp-cnv.prod[*Red Hat OpenShift Container Platform Cluster (AWS)*] catalog item in the Red Hat Demo Platform (RHDP) to run the hands-on exercises in this course.
 
 [TIP]
-The lab environment on average takes ~30-60 minutes to enter the ready state.  While your lab environment is provisioning, I recommend that you read through the course once, then return and complete the lab portions once your *OpenShift Container Cluster Platform* environment is ready to go. 
+The lab environment on average takes ~40-60 minutes to enter the ready state.  While your lab environment is provisioning, I recommend that you read through the course once, then return and complete the lab portions once your *OpenShift Container Cluster Platform* environment is ready to go. 
 
 // video::demohub_resources_v4.mp4[width=640]
 
@@ -27,9 +27,7 @@ The OCP environment will provide the foundation infrastructure for RHOAI. Once l
 
 == Operators and Red Hat OpenShift Container Platform
 
-Red Hat OpenShift Operators automate the creation, configuration, and management of instances of Kubernetes-native applications. Operators provide automation at every level of the stack—from managing the parts that make up the platform all the way to applications that are provided as a managed service.
-
-Red Hat OpenShift uses the power of Operators to run the entire platform in an autonomous fashion while exposing configuration natively through Kubernetes objects, allowing for quick installation and frequent, robust updates. In addition to the automation advantages of Operators for managing the platform, Red Hat OpenShift makes it easier to find, install, and manage Operators running on your clusters.
+Red Hat OpenShift Operators automate the creation, configuration, and management of instances of Kubernetes-native applications. Operators provide automation at every level of the stack—from managing the parts that make up the platform all the way to applications that are provided as a managed service. In addition to the automation advantages of Operators for managing the platform, Red Hat OpenShift makes it easier to find, install, and manage Operators running on your clusters.
 
 Included in Red Hat OpenShift is the Embedded OperatorHub, a registry of certified Operators from software vendors and open source projects. Within the Embedded OperatorHub you can browse and install a library of Operators that have been verified to work with Red Hat OpenShift and that have been packaged for easy lifecycle management.
 
@@ -67,83 +65,62 @@ This exercise uses the Red Hat Demo Platform; specifically the OpenShift Contain
 
 The following section discusses installing the *Red{nbsp}Hat OpenShift Serverless* operator.
 
+image::serverless_install.gif[width=600]
+
 1. Login to Red{nbsp}Hat OpenShift using a user which has the _cluster-admin_ role assigned.
 
 2. Navigate to **Operators** -> **OperatorHub** and search for *Red{nbsp}Hat OpenShift Serverless*
-+
-//image::serverless_operator_search.png[width=800]
 
 3. Click on the *Red{nbsp}Hat OpenShift Serverless* operator. In the pop up window, select the *stable* channel and the most recent version of the serverless operator. Click on **Install** to open the operator's installation view.
-+
-//image::serverless_operator_install1.png[width=600]
+
 
 4. In the `Install Operator` page, select the default values for all the fields and click *Install*.
-+
-//image::serverless_operator_install2.png[width=800]
+
 
 5. A window showing the installation progress will pop up.
-+ 
-//image::serverless_operator_install3.png[width=800]
 
 6. When the installation finishes the operator is ready to be used by *Red{nbsp}Hat OpenShift AI*.
-+
-//image::serverless_operator_install4.png[width=800]
+
 
 *Red{nbsp}Hat OpenShift Serverless* is now successfully installed.
 
 === Installation of Red Hat OpenShift Service Mesh Operator
 
 The following section discusses installing the *Red{nbsp}Hat OpenShift Service Mesh* operator.
 
+image::servicemesh_install.gif[width=600]
+
 1. Login to Red{nbsp}Hat OpenShift using a user which has the _cluster-admin_ role assigned.
 
 2. Navigate to **Operators** -> **OperatorHub** and search for *Red{nbsp}Hat OpenShift Service Mesh*
-+
-//image::servicemesh_operator_search.png[width=800]
 
 3. Click on the *Red{nbsp}Hat OpenShift Service Mesh* operator. In the pop up window, select the *stable* channel and the most recent version of the server mesh operator. Click on **Install** to open the operator's installation view.
-+
-//image::servicemesh_operator_install1.png[width=600]
 
 4. In the `Install Operator` page, select the default values for all the fields and click *Install*.
-+
-//image::servicemesh_operator_install2.png[width=800]
 
 5. A window showing the installation progress will pop up.
-+ 
-//image::servicemesh_operator_install3.png[width=800]
 
 6. When the installation finishes the operator is ready to be used by *Red{nbsp}Hat OpenShift AI*.
-+
-//image::servicemesh_operator_install4.png[width=800]
 
 *Red{nbsp}Hat OpenShift Service Mesh* is now successfully installed.
 
 === Installation of Red Hat Authorino Operator
 
 The following section discusses installing the *Red{nbsp}Hat - Authorino* operator.
 
+image::authorino_install.gif[width=600]
+
 1. Login to Red{nbsp}Hat OpenShift using a user which has the _cluster-admin_ role assigned.
 
 2. Navigate to **Operators** -> **OperatorHub** and search for *Red{nbsp}Hat Authorino
-+
-//image::authorino_operator_search.png[width=800]
 
 3. Click on the *Red{nbsp}Hat Authorino * operator. In the pop up window, select the *stable* channel and the most recent version of the serverless operator. Click on **Install** to open the operator's installation view.
-+
-//image::authorino_operator_install1.png[width=600]
 
 4. In the `Install Operator` page, select the default values for all the fields and click *Install*.
-+
-//image::authorino_operator_install2.png[width=800]
 
 5. A window showing the installation progress will pop up.
-+ 
-//image::authorino_operator_install3.png[width=800]
 
 6. When the installation finishes the operator is ready to be used by *Red{nbsp}Hat OpenShift AI*.
-+
-// image::authorino_operator_install4.png[width=800]
 
 *Red{nbsp}Hat Authorino* is now successfully installed.
 
@@ -152,12 +129,12 @@ The following section discusses installing the *Red{nbsp}Hat - Authorino* operat
 
  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. 
 
-=== Installation of Red Hat OpenShift AI Operator
+== Installation of Red Hat OpenShift AI Operator
 
+image::openshiftai_install.gif[width=600]
 
 * Navigate to **Operators** -> **OperatorHub** and search for *OpenShift AI*.
 
-//image::openshiftai_operator.png[width=640]
 
 . Click on the `Red{nbsp}Hat OpenShift AI` operator. In the pop up window that opens, ensure you select the latest version in the *fast* channel. Any version equal to or greater than 2.12 and click on **Install** to open the operator's installation view.  
 +
@@ -167,21 +144,20 @@ The following section discusses installing the *Red{nbsp}Hat - Authorino* operat
 . The operator Installation progress window will pop up. The installation may take a couple of minutes.
 
 
-//video::llm_dsc_v3.mp4[width=640]
-
 == Create OpenShift AI Data Science Cluster
 
 The next step is to create an OpenShift AI *Data Science Cluster (DSC)*.
 
 _A DataScienceCluster is the plan in the form of an YAML outline for Data Science Cluster API deployment. Manually editing the YAML configuration can adjust settings of the OpenShift AI DSC._
 
+image::dsc_install.gif[width=600]
+
 Return to the OpenShift Navigation Menu, Select Installed Operators, and click on the OpenShift AI Operator name to open the operator.
 
  . *Select the Option to create a Data Science Cluster.*
 
  . *Click Create* to deploy the Data Science Cluster.  
 
-//image::dsc_deploy_complete.png[width=640]
 
 == OpenShift AI install summary
 
@@ -200,26 +176,26 @@ Congratulations, you have successfully completed the installation of OpenShift A
 
 Navigate to the menu selector, located at the top right of the OCP dashboard.  Select the grid of squares, then select OpenShift AI.  At the logon screen, use the OCP admin credentials to login to OpenShift AI. 
 
+image::data_science_project.gif[width=600]
+
 Explore the dashboard navigation menus to familarize yourself with the options.
 
 Navigate to & select the Data Science Projects section.
 
  . Select the create data science project button.
 
- . Enter a name for your project, such as *fraud detection*.
+ . Enter a name for your project, such as *fraud-detection*.
 
  . The resource name should be populated automatically.
 
  . Optionally add a description to the data science project.
 
  . Select Create.
 
-//image::dsp_create.png[width=640]
 
 
-//== Creating a WorkBench 
 
-//video::openshiftai_setup_part3.mp4[width=640]
+
 
 Once complete, you should be on the landing page of the "fraud-detection" Data Science Project section of the OpenShift AI Console / Dashboard. 
 

modules/chapter1/nav.adoc

@@ -1,3 +1,3 @@
-* xref:index.adoc[]
-** xref:section1.adoc[]
-** xref:section2.adoc[]
+* xref:dsp-intro.adoc[]
+** xref:dsp-concepts.adoc[]
+** xref:section1.adoc[]

modules/chapter1/pages/section2.adoc → modules/chapter1/pages/dsp-concepts.adoc

@@ -7,6 +7,8 @@ A pipeline is an execution graph of tasks, commonly known as a _DAG_ (Directed A
 A DAG is a directed graph without any cycles, i.e. direct loops.
 ====
 
+== Why data science pipelines
+
 A data science pipeline is typically implemented to improve the repeatability of a data science experiment.  While the larger experimentation process may include steps such as data exploration, where data scientists seek to create a fundamental understanding of the characteristics of the data, data science pipelines tend to focus on turning a viable experiment into a repeatable solution that can be iterated on.
 
 A data science pipeline, may also fit within the context of a larger pipeline that manages the complete lifecycle of an application, and the data science pipeline is responsible for the process of training the machine learning model.

modules/chapter1/pages/index.adoc → modules/chapter1/pages/dsp-intro.adoc

@@ -6,6 +6,8 @@ A machine learning pipeline is a crucial component in the development and produc
 
 Machine learning (ML) pipelines are a key part of the data science process, helping data scientists to streamline their work and automate tasks. They can make the model development process more efficient and reproducible, while also reducing the risk of errors.
 
+=== Data science pipeline benefits:
+
 *Modularization:* Pipelines enable you to break down the machine learning process into modular, well-defined steps. Each step can be developed, tested and optimized independently, making it easier to manage and maintain the workflow.
 
 *Reproducibility:* Machine learning pipelines make it easier to reproduce experiments. By defining the sequence of steps and their parameters in a pipeline, you can recreate the entire process exactly, ensuring consistent results. If a step fails or a model's performance deteriorates, the pipeline can be configured to raise alerts or take corrective actions.
@@ -16,6 +18,8 @@ Machine learning (ML) pipelines are a key part of the data science process, help
 
 *Version control and documentation:* You can use version control systems to track changes in your pipeline's code and configuration, ensuring that you can roll back to previous versions if needed. A well-structured pipeline encourages better documentation of each step.
 
+=== Machine learning lifecycles & DevOps
+
 Machine learning lifecycles can vary in complexity and may involve additional steps depending on the use case, such as hyperparameter optimization, cross-validation, and feature selection. The goal of a machine learning pipeline is to automate and standardize these processes, making it easier to develop and maintain ML models for various applications.
 
 Integration with DevOps (2010s): Machine learning pipelines started to be integrated with DevOps practices to enable continuous integration and deployment (CI/CD) of machine learning models. This integration emphasized the need for reproducibility, version control and monitoring in ML pipelines. This integration is referred to as machine learning operations, or MLOps, which helps data science teams effectively manage the complexity of managing ML orchestration. In a real-time deployment, the pipeline replies to a request within milliseconds of the request.

modules/chapter1/pages/section1.adoc

@@ -1,7 +1,20 @@
-= blah blah blah
+= RHOAI DSP specifics
 
 
-This is just reference info -tb updated
+== Specific Data Science Pipeline terminology in OpenShift AI DSP
+
+ . *Pipeline* - is a workflow definition containing the steps and their input and output artifacts.
+
+ . *Run* - is a single execution of a pipeline. A run can be a one off execution of a pipeline, or pipelines can be scheduled as a recurring run.
+
+ . *Task* - is a self-contained pipeline component that represents an execution stage in the pipeline.
+
+ . *Artifact* - Steps have the ability to create artifacts, which are objects that can be persisted after the execution of the step completes. Other steps may use those artifacts as inputs and some artifacts may be useful references after a pipeline run has completed. Artifacts automatically stored by Data Science Pipelines in S3 compatible storage.
+
+ . *Experiment* - is a logical grouping of runs for the purpose of comparing different pipelines
+
+ . *Execution* -  is an instance of a Task/Component
+
 
 === Data Science Pipelines