Red Hat OpenShift AI Technical Overview Training

Last Updated: 18 08 2025

This free AI technical observation course provides a comprehensive introduction to the current AI/ML landscape, which exposes opportunities and challenges in developing and deploying real-world AI-operated applications. The curriculum has an on-demand video content that guides learners through the management of full AI/mL life cycle using modern equipment, outline, and industry practices-from model development to deployment and real-time monitoring. 
 
An important focus of the course is MLOps, which plays an important role in streamlining workflows, enabling cooperation and ensuring scalability, safety, and stability of AI projects in the enterprise environment. The course also explains how Red Hat OpenShift AI enhances the capabilities of Red Hat OpenShift by offering a consistent, safe and hybrid-tier platform. 
 
This strong AI platform data simplifies cooperation between scientists and developers, which enables rapid model training, reliable purpose pipelines, and efficient life cycle management. The learners will also know that OpenShift's cloud infrastructure supports large-scale AI initiatives by providing flexibility, high availability. 

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Learning Options for You

  • Live Training (Duration : 16 Hours)
  • Per Participant

Fee: On Request

Course Prerequisites

Participants should have a foundational understanding of Linux systems and containers, as well as a basic familiarity with Kubernetes concepts. Prior experience with data science or machine learning workflows will help learners better contextualise how OpenShift AI supports MLOps practices. 

  • Basic knowledge of the Linux operating system 
  • Familiarity with container technologies (e.g., Docker) 
  • General awareness of Kubernetes concepts 
  • Introductory understanding of data science or machine learning processes (recommended) 

Learning Objectives

This course aims to introduce learners to the core concepts, architecture, and capabilities of Red Hat OpenShift AI. Participants will explore how OpenShift AI supports the entire machine learning lifecycle, from data preparation to model deployment, using container-native tools and scalable infrastructure. By the end of the course, learners will understand how to leverage OpenShift AI to streamline collaboration between data science and operations teams, accelerating time to value for AI projects. 

Target Audience

  • Data scientists and data engineers 
  • Machine learning engineers 
  • DevOps professionals supporting AI/ML workflows 
  • Platform engineers managing container platforms 
  • IT architects and solution designers 
  • Professionals interested in deploying AI/ML workloads on OpenShift 

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