AI268 Developing and Deploying AI/ML Applications on Red Hat OpenShift AI with Exam Course
- Duration: 32 Hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
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Course Overview
The AI268 – Developing and Deploying AI/ML Applications on Red Hat OpenShift AI with Exam Course equips learners with the expertise to leverage Red Hat OpenShift AI for machine learning workflows. Participants explore the OpenShift AI architecture, manage data science projects, work with Jupyter notebooks, and learn to train and enhance models using platform‑specific tooling such as RHOAI. The curriculum also covers model serving and the creation, monitoring, and control of data science pipelines using frameworks such as Kubeflow and Elyra. By combining hands‑on practice with certification preparation, this training prepares professionals to deploy and operationalize AI/ML applications at scale.
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Course Details
- Learn foundational OpenShift AI architecture and key components for AI/ML workflows
- Gain hands on experience building and training models with Jupyter notebooks and OpenShift AI workbenches
- Deploy and serve machine learning models using integrated model serving tools
- Design and manage data science pipelines with Kubeflow and Elyra for workflow automation
- Prepare for the EX267 certification exam, validating your ability to configure, support, and operate AI/ML applications on OpenShift AI
- Support collaboration between data science and DevOps teams, enabling smoother production deployments and automated model lifecycle management
- Ideal for data scientists, AI/ML developers, MLOps engineers, and cloud architects looking to operationalize machine learning using enterprise platforms
- Required experience with Git and Python development, or completion of equivalent foundational training such as Python Programming with Red Hat (AD141)
- Familiarity with Red Hat OpenShift fundamentals is required, or completion of Red Hat OpenShift Developer II (DO288)
- Basic exposure to machine learning concepts and data science workflows enhances learning outcomes
- Explain the architecture and capabilities of Red Hat OpenShift AI and associated components
- Create and manage data science projects and interactive development environments with Jupyter notebooks
- Train machine learning models using default and customized workbenches within the OpenShift AI environment
- Enhance model training using RHOAI best practices
- Deploy and manage model serving infrastructure for production ready inference
- Build, run, and monitor data science pipelines using tools like Kubeflow and Elyra
- Demonstrate readiness for the EX267 certification exam through targeted exam preparation exercises
- Introduction to Red Hat OpenShift AI: Architecture, components, and use cases
- Introduction to Red Hat OpenShift AI: Architecture, components, and use cases
- Data Science Projects: Organizing code, workbenches, and data sources
- Jupyter Notebooks: Using interactive notebooks for experimentation
- Installing and Managing OpenShift AI: Platform setup and component governance
- User and Resource Management: Allocation policies and collaborative workflows
- Custom Notebook Images: Creating and using tailored environments
- Machine Learning Fundamentals: Core concepts and workflows
- Training and Enhancing Models: Hands on training and optimization
- Model Serving: Strategies for serving and scaling models
Beyond Training | Our Learning Community in Action
We regularly host alumni meetups, expert sessions, and networking events to help professionals stay updated, connected, and industry-ready even after course completion.
“ Alumni meetups that keep professionals connected, visible, and engaged even after completing their training journey. ”
“ Learner gatherings designed to strengthen peer connections, real-world networking, and shared growth opportunities. ”
“ Expert-led sessions that help professionals stay updated with practical insights, trends, and industry perspectives. ”
“ A growing community experience built around collaboration, industry readiness, and continuous professional development. ”
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