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

<p>The Red Hat Certified Specialist in OpenShift AI (EX267) course delivers focused, hands-on training to help you build, train, deploy, and manage AI and machine learning workloads on Red Hat OpenShift. Through practical labs and real-world scenarios, you will learn to work with data science projects, Jupyter notebooks, model training, model serving, and automated ML pipelines—developing the skills required to run enterprise-grade AI solutions on a modern OpenShift platform.</p>

Course Description

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

Course curriculum

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

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

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

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

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

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

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

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

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

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

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

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

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

  • Manage pipeline artifacts and parameters
  • Create and monitor ML experiments
  • Evaluate and optimize pipeline runs
  • Live classes (Online & Classroom)
  • led by industry experts
  • hands-on experience
  • 50+ sessions
  • 218 Hours
  • 4 Month
  • Online / Clsseoom
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Our Certification Holders

Join a community of professionals certified in Red Hat OpenShift AI (EX267), validating hands-on expertise in building, training, deploying, and managing AI and machine learning workloads on OpenShift. This certification opens doors to modern roles in AI engineering, MLOps, data science, and cloud-native AI platforms across global organizations.

Why Enroll for GRRAS Red Hat OpenShift AI (EX267)?

Industry-Aligned Curriculum

Master an exam-focused EX267 curriculum designed by Red Hat experts. Learn to build, train, deploy, and manage AI/ML workloads on OpenShift, covering data science projects, model training, model serving, and automated ML pipelines.

Personalized Career Support

Get one-on-one career guidance, resume support, mock interviews, and placement assistance. GRRAS helps you confidently transition into AI engineering, MLOps, and cloud-native data science roles.

Expert Mentorship

Learn from certified Red Hat professionals with real production experience. Gain practical insights into OpenShift AI workflows, troubleshooting, and best practices—ensuring exam readiness and job confidence.

Real-World Projects

Work on enterprise-grade OpenShift AI scenarios, including end-to-end ML pipelines, model deployment, and monitoring—building production-ready AI and MLOps skills.

600+ Hiring Partners Across Industries

Our extensive network of hiring partners spans various industries, offering diverse opportunities to kickstart your career.

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