Developing and Deploying AI/ML Applications on Red Hat OpenShift AI with Exam
Developing and Deploying AI/ML Applications on Red Hat OpenShift AI teaches students to build, train, and deploy machine learning models efficiently. With hands-on training, they’ll learn to manage AI/ML workloads and automate workflows using OpenShift AI 2.13 on Red Hat OpenShift 4.16. The course includes the Red Hat Certified Specialist in OpenShift AI Exam (EX267).
Course Overview
Developing and Deploying AI/ML Applications on Red Hat OpenShift AI teaches students to build, train, and deploy machine learning models efficiently. Moreover, with hands-on training, they’ll learn to manage AI/ML workloads and automate workflows using OpenShift AI 2.13 on Red Hat OpenShift 4.16. In addition, the course includes the Red Hat Certified Specialist in OpenShift AI Exam (EX267), thereby providing learners with both practical skills and a recognized certification.
Course Content Summary
To ensure structured learning, the course is divided into the following modules:
Introduction to Red Hat OpenShift AI – establishing foundational knowledge.
Data Science Projects – applying concepts to real-world scenarios.
Jupyter Notebooks – exploring interactive development environments.
Red Hat OpenShift AI Installation – setting up the platform.
User and Resources Management – allocating and controlling resources effectively.
Custom Notebook Images – tailoring environments to project needs.
Introduction to Machine Learning – covering essential ML principles.
Training Models – building and refining models step by step.
Enhancing Model Training with RHOAI – leveraging platform-specific optimizations.
Introduction to Model Serving – understanding model deployment.
Model Serving in Red Hat OpenShift AI – implementing serving in practice.
Introduction to Data Science Pipelines – streamlining workflows.
Working with Pipelines – executing and managing pipelines.
Controlling Pipelines and Experiments – ensuring scalability and reproducibility.
Recommended Training
Before enrolling, it is important to note the recommended prerequisites:
Git experience (required) – as version control is essential.
Python development experience or completion of Python Programming with Red Hat (AD141) – since Python is widely used in AI/ML.
Red Hat OpenShift experience or completion of Red Hat OpenShift Developer II: Building and Deploying Cloud-Native Applications (DO288) – to build on prior knowledge.
Basic knowledge of AI, data science, and machine learning (recommended) – to better grasp advanced topics.
Course Outline
Getting Started with Red Hat OpenShift AI – Explore key features and understand how everything fits in the Red Hat AI ecosystem.
Data Science Projects – Organize code, configurations, and data connections efficiently.
Jupyter Notebooks – Run and test code interactively in real time.
Installing Red Hat OpenShift AI – Learn installation steps and component management.
Managing Users and Resources – Allocate resources and manage user access effectively.
Custom Notebook Images – Create and import custom notebook images.
Machine Learning Basics – Understand ML fundamentals, types, and workflows.
Training Models – Gain hands-on experience training models on workbenches.
Enhancing Training with RHOAI – Apply best practices in ML and data science.
Model Serving Fundamentals – Learn to export, share, and serve ML models.
Serving Models with OpenShift AI – Deploy and serve models in practice.
Data Science Pipelines 101 – Introduction to pipelines setup.
Building Pipelines – Create pipelines using Kubeflow SDK and Elyra.
Managing Pipelines and Experiments – Configure, monitor, and track pipelines with metrics and artifacts.
Impact on Your Organization
To begin with, organizations generate and store massive amounts of data from various sources. As a result, Red Hat OpenShift AI provides a powerful platform to analyze data, uncover trends, and make predictions using machine learning and AI algorithms. Consequently, this helps businesses turn raw data into actionable insights that drive smarter decisions and improve overall efficiency.
Impact on the Individual
On the other hand, by the end of this course, you’ll have a solid understanding of Red Hat OpenShift AI’s architecture and how to use it effectively. More specifically, you’ll learn to install and manage OpenShift AI, allocate resources, update components, and control user access. In addition, you’ll gain hands-on experience in training, deploying, and serving machine learning models, while also applying best practices in AI and data science. Finally, as a culmination of your learning, you’ll be able to define and set up data science pipelines, thereby streamlining workflows for scalable AI/ML projects.
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