Developing and Deploying AI/ML Applications on Red Hat OpenShift AI Overview

Last Updated: 18 08 2025

This course provides a wide introduction to developing and managing AI/ML applications using Red Hat OpenShift AI. It is designed to help learners understand how to deploy the machine learning model in the enterprise environment. The course includes important concepts such as handling large datasets, designing machine learning pipelines, training and adapting models, and deploying them efficiently with Red Hat OpenShift AI platforms.

The course emphasises practical, hands-on learning. You will gain experience working with major devices such as Jupiter notebooks, opening pipelines and interactive labs. These laboratories are designed to give you the real-world experience in the manufacture, testing and deployment of AI/ML applications within the OpenShift Ecosystem.

By the end of the course, the learners will be equipped with the skills and confidence to manage advanced AI/ML projects. Whether you are a data scientist, developer, or machine learning engineer, this course provides the knowledge that you need to work effectively with AI on the Red Hat OpenShift. With practical applications and a focus on enterprise-level equipment, you will specialise in applying AI/ML solutions on a scale that requires this course to move forward in the field of AI/ML development.

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Learning Options for You

  • Live Training (Duration : 32 Hours)
  • Per Participant

Fee: On Request

Course Prerequisites

Before enrolling, participants should be familiar with basic programming concepts, preferably in Python, as well as fundamental statistics. Prior exposure to containers and cloud technologies is helpful but not strictly necessary. The course is designed to introduce OpenShift AI, so beginners to container orchestration can still benefit as long as they are willing to learn. 

  • Basic knowledge of Python programming (mandatory) 
  • Understanding of basic statistics and data analysis concepts (recommended) 
  • Familiarity with container or cloud environments (helpful but not required) 

Learning Objectives

This course aims to provide learners with a strong foundation in building machine learning models using Python, while leveraging Red Hat OpenShift AI for scalable, containerised deployment. Participants will explore the essential steps in the machine learning pipeline — from data preparation to model training and evaluation — and learn how to operationalise these models in a production-ready OpenShift environment. By the end of the course, learners will be able to build, deploy, and manage basic machine learning solutions more efficiently and effectively. 

Target Audience

  • Data analysts and data scientists 
  • Software developers interested in AI and ML 
  • DevOps and MLOps engineers 
  • IT professionals exploring containerised ML workflows 
  • Students and professionals looking to get started with machine learning 
  • Anyone interested in deploying ML models with Red Hat OpenShift AI 

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