lecture(ch01s01): Intro to RHODS

antora-playbook.yml

@@ -9,6 +9,8 @@ content:
 asciidoc:
   attributes:
     page-pagination: true
+    rhods-major-version: 1.28
+    ocp-version: 4.12
 
 ui:
   bundle:

modules/ROOT/pages/index.adoc

@@ -1,21 +1,22 @@
-= Developing Intelligent Applications using Red Hat OpenShift Data Science (RHODS)
+= Developing AI-enabled Applications with Red{nbsp}Hat OpenShift Data Science
 :navtitle: Welcome
 
-== Introduction
-
-IMPORTANT: This is not an official Red Hat Training course. Please contact your Red Hat sales representative if you want more information about Training or Guidelines about this product. You can view the list of training courses and certifications at [https://www.redhat.com/en/services/training-and-certification].
+IMPORTANT: This is not an official Red{nbsp}Hat Training course. Please contact your Red{nbsp}Hat sales representative if you want more information about Training or Guidelines about this product. You can view the list of training courses and certifications at [https://www.redhat.com/en/services/training-and-certification].
 
 NOTE: This course is under development.
 
-Welcome to the Red Hat Training quick course on building intelligent applications using Red Hat OpenShift Data Science (RHODS).
+Welcome to the Red{nbsp}Hat Training quick course on building AI-enabled applications with Red{nbsp}Hat OpenShift Data Science (RHODS).
 
 Use the links in the sidebar panel to navigate this course.
 
-Companies collecting and storing vast amounts of information from multiple sources need an easy way to analyze this data, visualize trends and patterns, and experiment with predicting future business outcomes using machine learning and artificial intelligence algorithms. 
+== Introduction
+
+Companies collecting and storing vast amounts of information from multiple sources need an easy way to analyze this data, visualize trends and patterns, and experiment with predicting future business outcomes using machine learning and artificial intelligence algorithms.
 
-This course addresses the customer need for an introduction to using Red Hat OpenShift Data Science (RHODS) that includes basic concepts and tools. As a result of taking this course, learners will be able to perform data analysis, create  learning models, and deploy them for consumption by applications.
+This course addresses the customer need for an introduction to using RHODS, including basic concepts and tools.
+After taking this course, learners should be able to perform data analysis, create learning models, and deploy them for consumption by applications.
 
-This course is based on Red Hat® OpenShift® Container Platform 4.12 and Red Hat® OpenShift® Data Science 1.25.
+This course is based on Red{nbsp}Hat® OpenShift® Container Platform {ocp-version} and Red{nbsp}Hat® OpenShift® Data Science {rhods-major-version}.
 
 == Course Objectives
 
@@ -30,7 +31,7 @@ At a foundational level:
 == Audience
 
 * Data scientists interested in using RHODS to perform data analysis, visualization, training data sets, and serving machine learning models for consumption by applications.
-* Developera who want to learn about integrating applications with RHODS to provide data analysis, data visualization, machine learning and predictive capabilities.
+* Developers who want to learn about integrating applications with RHODS to provide data analysis, data visualization, machine learning and predictive capabilities.
 
 ==  Prerequisites
 

modules/ROOT/partials/under-development.adoc

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+NOTE: This section is under development.

modules/intro/pages/intro.adoc

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-= Introduction to RedHat OpenShift Data Science
+= Introduction to Red{nbsp}Hat OpenShift Data Science
+
+include::ROOT:partial$under-development.adoc[]
+
+video::Y12T8G1LpyY[youtube,title=Demo: Introduction to RHODS,width=640,height=480]
+
+// ==== Product Owner Notes ====
+// * Introduction to RHODS - what problem it solves (conceptual overview)
+// * What the student can expect to accomplish by the end of the course
+// * We want to try something new - record a full end to end demo of using RHODS for a specific business case. For example - data analysis, visualization and a simple machine learning and serving use case. We want to hook the student in. They are not expected to understand all the details. It’s just “look at all the cool things you can do. You will be able to do this at the end of this course”. Provide a fully runnable, complete Jupyter Notebook.
+// * Embed the demo video in the lecture section. No need for a separate section.
 
 == Objective
 
-* Become familiar with the general architecture and main features of Red Hat OpenShift Data Science
+* Become familiar with the general architecture and main features of Red{nbsp}Hat OpenShift Data Science
 
-== The RedHat OpenShift Data Science Platform
 
-* Introduction to RHODS - what problem it solves (conceptual overview)
-* What the student can expect to accomplish by the end of the course
-* We want to try something new - record a full end to end demo of using RHODS for a specific business case. For example - data analysis, visualization and a simple machine learning and serving use case. We want to hook the student in. They are not expected to understand all the details. It’s just “look at all the cool things you can do. You will be able to do this at the end of this course”. Provide a fully runnable, complete Jupyter Notebook.
-* Embed the demo video in the lecture section. No need for a separate section.
+== The Complexity of AI Applications
 
-video::Y12T8G1LpyY[youtube,title=Demo: Introduction to RHODS,width=640,height=480]
+Data scientists commonly struggle to effectively deliver their artificial intelligence (AI) models to customers.
+As a data scientist, in some cases, you might lack the software engineering ability to create a serving layer that exposes the model.
+You might also struggle with the operational part, by administering the infrastructe required to train and serve a model.
+
+As any other piece of software, AI-based applications follow a lifecycle.
+If you do not have access to a consistent platform that allows you to move through this lifecycle, then your ability to deliver AI solutions can be seriously impacted.
+
+As well as classic software engineering phases, such as deployment, or monitoring, AI systems bring additional requirements into their life cycle:
+
+* Teams must be capable to collect, store, read, verify, and preprocess data.
+* Then, they must have the ability to train data models by running multiple experiments, and be able to quickly reproduce those experiments.
+* They must also be able to serve a model, scale it up when necessary to meet the demands, or scale it down to save costly resources, such as GPUs.
+* Finally, the must be able to monitor the accuracy of the model in production, and detect any potential deviations from the expected accuracy and performance.
+
+== Red{nbsp}Hat OpenShift Data Science
+
+
+Red{nbsp}Hat OpenShift Data Science (RHODS) is a platform that enables enterpises to train, build, deploy, and monitor AI-enabled applications.
+RHODS is the central piece of Red{nbsp}Hat OpenShift AI, a portfolio of products to cover the complete life cycle of AI applications, models, and workloads.
+
+With RHODS, teams add a common platform to operate the complete lifecycle of AI-enabled applications:
+
+* Data scientists can start training their models on a common JupyterLab interface, which they are familiar with.
+They do not need to configure environments because their workbenches run on Red{nbsp}Hat OpenShift.
+
+* Software and Machine Learning Engineers can configure pipelines to integrate and deploy the models that result from Jupyter notebooks.
+
+* Cluster administrators can provide container images as customized working environments for data scientists, so that data scientists do not need to care about dependencies.
+They can also set quotas and scaling policies to optimize resource consumption.
+
+== RHODS Architecture
+
+RHODS is based on the Open Data Hub upstream project.
+Open Data Hub is an open source platform to handle AI lifecycles in hybrid clouds.
+It is based on Kubernetes, OpenSfhit, and operators.
+
+RHODS incorporates the following elements:
+
+* Custom Jupyter-based environments on demand, called _workbenches_.
+* An set of curated and tested container workbench images, ready for data scientists to start working
+* Tested, certified, and supported integrations with the most popular AI technologies, such as Tensorflow, and PyTorch, among others.
+* Community-driven integrations, such as Airflow or mlflow.
+* A Model Serving framework to streamline model deployment and serving.
+* A UI console, integrated on OpenShift.
+
+
+image::rhods-arch.png[title="RHODS components"]
 
 === References
 
-* https://developers.redhat.com/learn/openshift-data-science[Red Hat Developers page for RHODS]
+* https://developers.red{nbsp}hat.com/learn/openshift-data-science[Red{nbsp}Hat Developers page for RHODS]
+* https://opendatahub.io/[Open Data Hub]
+* For more information, refer to the _Getting Started with Red{nbsp}Hat OpenShift Data Science_ documentation at https://access.red{nbsp}hat.com/documentation/en-us/red_hat_openshift_data_science/1/html-single/getting_started_with_red_hat_openshift_data_science/index