EX267: Red Hat Certified OpenShift AI

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Why Red Hat OpenShift AI Training from GRRAS?

Build in-demand AI and machine learning skills with Red Hat OpenShift AI (EX267) training from GRRAS. Learn how to design, train, deploy, and manage AI/ML workloads on OpenShift through hands-on labs covering data science projects, model training, model serving, and automated ML pipelines. This exam-focused, real-world training helps you master OpenShift AI tools such as Jupyter notebooks, model servers, and Kubeflow pipelines, enabling you to earn a globally recognized Red Hat certification and prepare for modern roles in AI engineering, MLOps, and cloud-native data science.

Course Snapshot

What is Red Hat OpenShift AI (EX267)?

The Red Hat Certified Specialist in OpenShift AI (EX267) training is a hands-on, exam-focused program designed to teach you how to build, train, deploy, and manage AI and machine learning workloads on Red Hat OpenShift. This course enables organizations to operationalize AI models at scale using a secure, cloud-native platform.

Through structured modules and real-world labs, you will learn to create data science projects, work with Jupyter notebooks, train machine learning models, deploy models for inference, and automate ML workflows using pipelines within OpenShift AI environments.

Course Structure & Timeline

Total Duration: 32+ hours of instructor-led OpenShift AI training
Regular Format: 16 days (ideal for working professionals)
Fast-Track Format: 5 days (accelerated, intensive program)
Delivery Mode: Hands-on labs, guided demonstrations, and exam-oriented practice
Technology Stack: Red Hat OpenShift Container Platform, Red Hat OpenShift AI, Jupyter, Kubeflow Pipelines (exam-aligned version)

Who Should Enroll

Data Scientists: Professionals developing and training machine learning models
ML Engineers & MLOps Engineers: Practitioners operationalizing ML models on OpenShift
AI & Analytics Professionals: Teams building AI-driven applications and workflows
DevOps & Platform Engineers: Engineers supporting AI workloads on Kubernetes and OpenShift
Cloud & OpenShift Administrators: Admins managing AI platforms and resources
Certification Aspirants: Learners preparing for the Red Hat EX267 certification exam

EX267 Syllabus Structure

The EX267 course provides focused, hands-on training in Red Hat OpenShift AI to help you build, train, deploy, and manage machine learning models on OpenShift. Through practical labs and real-world exercises, you will learn to work with data science projects, Jupyter notebooks, model training and serving, and automated ML pipelines—strengthening your cloud-native AI skills and preparing you to confidently clear the EX267 certification exam.

Download the complete EX267: Red Hat Certified OpenShift AI

Introduction to Red Hat OpenShift AI

Understand OpenShift AI architecture and core components
Explore key services and platform capabilities
Integrate OpenShift AI with OpenShift Container Platform

Data Science Projects

Create and manage data science projects in OpenShift AI
Work with workbenches and data connections
Perform hands-on project and data setup exercises

Jupyter Notebooks

Use Jupyter notebooks for AI/ML development
Collaborate and share notebooks across teams
Build and test notebooks through guided labs

Red Hat OpenShift AI Installation

Install OpenShift AI components
Configure and validate platform installation
Verify setup using guided exercises

User and Resource Management

Manage users, roles, and permissions
Allocate and monitor compute and storage resources
Control resource limits and access efficiently

Custom Notebook Images

Create and import custom notebook images
Configure dependencies and runtime environments
Build and test images using guided labs

Introduction to Machine Learning

Understand core ML concepts and workflows
Learn data preprocessing, training, and evaluation
Manage ML lifecycle within OpenShift AI

Training Models

Train ML models using OpenShift AI workbenches
Use preconfigured and custom training environments
Develop and test models through hands-on labs

Enhancing Model Training with OpenShift AI

Monitor workbench and training resources
Scale and optimize ML workloads
Track and visualize training performance metrics

Introduction to Model Serving

Understand AI model serving fundamentals
Save and manage trained models for inference
Deploy and test models using guided labs

Model Serving in Red Hat OpenShift AI

Deploy and manage models using model servers
Consume models via APIs for predictions
Create and manage multiple model servers

Introduction to Data Science Pipelines

Understand purpose and structure of ML pipelines
Create pipelines for automated workflows
Connect stages of data science projects

Working with Pipelines

Build pipelines using Kubeflow SDK
Design workflows with Elyra visual pipelines
Create and run custom pipelines in labs

Controlling Pipelines and Experiments

Manage pipeline artifacts and parameters
Create and monitor ML experiments
Evaluate and optimize pipeline runs

Get ahead in your career by learning Top AI Tools Title

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EX267: Red Hat Certified OpenShift AI Tool Kits

Automation

Configuration

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