AI-267 Red Hat OpenShift AI Model Training Course
Length
4 days / 4 weeks
Price
Days
Mon - Fri
Why Choose This Course
Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) teaches you how to build, train, deploy, and operate machine learning workloads on Red Hat OpenShift AI using guided, instructor-led training and hands-on practice. You learn the core capabilities of OpenShift AI including workbenches such as Jupyter notebooks, resource and user management, custom notebook images, model training, model serving, and automation with data science pipelines. The curriculum is current to Red Hat OpenShift 4.16 and Red Hat OpenShift AI 2.13, ensuring you train on features and workflows used in production today.
The course is relevant for professionals who need to operationalise AI in containerised, cloud-native environments. It equips developers, data scientists, and MLOps engineers to collaborate on a single platform, from experimentation to deployment, with practical labs that mirror enterprise scenarios. You gain experience organising data science projects, allocating resources safely, and serving trained models to applications, all within the governance of OpenShift AI.
By the end, you will be able to install and manage OpenShift AI components, enable collaborative model development, deploy and serve models for inference, and automate end-to-end machine learning workflows with pipelines and experiments. The training emphasises practical value for roles implementing AI-enabled applications on Kubernetes platforms used widely across industries. A certificate of course attendance is included.
Events
Prerequisites
- There are no formal prerequisites for this course.
Exam
Candidates can achieve this certification by passing the following exam(s).
- Red Hat Certified Specialist in OpenShift AI exam (EX267).
Books
- Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) course material included.
Delivery
- Live virtual online training attend in real-time from anywhere
Skills Gained
- Identify OpenShift AI architecture, components, and capabilities.
- Create and manage data science projects, workbenches, and data connections.
- Use Jupyter notebooks for interactive development and testing.
- Install and configure Red Hat OpenShift AI in an OpenShift environment.
- Manage users, roles, and resource allocation for AI workloads.
- Build and import custom notebook images to standardise environments.
- Apply core machine learning concepts and workflows in OpenShift AI.
- Train models using default and custom workbenches; save and load models.
- Enhance training using OpenShift AI best practices for data science.
- Serve trained models using OpenShift AI model serving.
- Create, run, and manage data science pipelines.
- Track and control experiments and pipelines for reproducibility.
Audience
- Data scientists and AI practitioners building and training ML models on OpenShift AI.
- Application developers integrating AI/ML capabilities into cloud-native services.
- MLOps engineers responsible for operationalising the ML lifecycle on OpenShift AI.
- System or software architects and administrators supporting AI-enabled applications.
Course Schedule & Pricing
Choose the schedule that fits your life — all options include full course materials & certification support
Full-time immersion for rapid certification readiness.
Balance your career while you upgrade your skills.
Maximum flexibility for busy working professionals.
Outline
- Introduction to Red Hat OpenShift AI and its architecture
- Capabilities and use cases of OpenShift AI in enterprise settings
- Creating data science projects and workspaces
- Managing workbenches for team collaboration
- Configuring data connections for projects
- Using Jupyter notebooks for interactive development
- Installing OpenShift AI components in an OpenShift cluster
- Configuring and updating OpenShift AI services
- User, role, and resource management for AI workloads
- Setting quotas and allocating resources for workbenches
- Building custom notebook images for consistent environments
- Introduction to machine learning concepts and workflows
- Training models from notebooks and workbenches
- Persisting, saving, and loading trained models
- Applying OpenShift AI features to enhance training practices
- Model serving concepts and components
- Deploying and managing model serving in OpenShift AI
- Introduction to data science pipelines
- Authoring and running pipelines for ML workflows
- Managing pipeline runs, artifacts, and dependencies
- Tracking experiments and controlling pipelines for reproducibility
- Exam-aligned practice areas mapped to EX267 objectives
Terms & Conditions
Frequently Asked Questions (FAQ's)
What is Red Hat OpenShift AI and how does it relate to Kubernetes?
How does this course help me prepare for the EX267 certification?
Is the training hands-on?
Our Partnership
Reliable certification testing is vital for validating professional skills in today’s tech-driven world. As a Pearson VUE Authorised Centre, we provide a secure environment for globally recognised IT exams. This partnership ensures convenient access to certifications with the highest standards of integrity and accuracy.
Our Accreditations
