AI-296 Red Hat Enterprise Linux AI Certification Training course
Length
2 days / 2 weeks
Price
Days
Mon - Fri
Learn More
Why Choose This Course
Develop, Test, and Run Granite Family LLMs with Red Hat Enterprise Linux AI (AI296) is an instructor-led course focused on how to fine-tune, serve, and operate enterprise-ready large language models using Red Hat Enterprise Linux AI. The curriculum covers generative AI fundamentals, Granite models for enterprise use, and the end-to-end workflow to train and deploy models in a secure environment. The current release is based on Red Hat Enterprise Linux AI 1.5, so learners work with up-to-date features and tooling.
The course helps professionals who need to learn how to prepare and adapt LLMs for specific business scenarios while maintaining governance and privacy. You explore how Granite models address enterprise use cases, and you practice training and serving models on Red Hat Enterprise Linux AI with production‑oriented methods. This prepares you to integrate AI capabilities into applications and workflows with practical, exam‑aligned content and hands‑on activities.
Labs and examples reflect real-world scenarios used in training exercises, including working with synthetic datasets to accelerate experimentation. By the end of the course, you will be able to select an appropriate Granite model, fine‑tune it with curated data, serve it for inference, and operate it with confidence on Red Hat Enterprise Linux AI. A certificate of course attendance is included.
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
- AI296 course material included.
Delivery
- Live virtual online training attend in real-time from anywhere
Skills Gained
- Explain core concepts, benefits, and challenges of generative AI for enterprises.
- Describe the Granite family of models and when to choose them for business use cases.
- Select suitable models and techniques for specific tasks, considering capabilities and limitations.
- Prepare curated or synthetic data to adapt foundation models for domain needs.
- Fine‑tune large language models using Red Hat Enterprise Linux AI workflows.
- Serve trained models for inference on Red Hat Enterprise Linux AI.
- Compare fine‑tuning and retrieval‑augmented generation to choose an approach.
- Use Red Hat AI Inference Server concepts to plan fast, cost‑effective deployments.
- Operate and monitor deployed models in secure, private environments.
- Collaborate across technical and non‑technical stakeholders when building LLM solutions.
- Understand basic hardware considerations for GPU‑accelerated inference on supported platforms.
- Align learning to the Red Hat AI skills path toward OpenShift AI specialization.
Audience
- Data scientists and AI specialists adapting LLMs to enterprise scenarios.
- Developers and machine learning engineers building AI‑enabled applications.
- System administrators supporting secure AI platforms and deployments.
- Subject matter experts contributing domain knowledge for model alignment.
Course Schedule & Pricing
Choose the schedule that fits your life — all options include full course materials & certification support
Weekdays
Mon - Fri
📅 02 days
☀️ 9:30 am – 5 pm
$
Full-time immersion for rapid certification readiness.
Weeknights
Mon & Tue
📅 02 weeks
🌙 6 pm – 9 pm
$
Balance your career while you upgrade your skills.
Weekends
Saturdays Only
📅 02 weeks
☀️ 9:30 am – 5 pm
$
Maximum flexibility for busy working professionals.
Outline
- Generative AI concepts: definitions, benefits, and challenges for enterprises
- Assessing model capabilities and limits for different tasks
- Granite models overview and selection criteria for business use
- Preparing datasets for LLM adaptation, including synthetic data options
- Red Hat Enterprise Linux AI architecture and tooling overview
- Fine‑tuning workflows on Red Hat Enterprise Linux AI
- Comparing fine‑tuning and retrieval‑augmented generation approaches
- Serving models for inference on Red Hat Enterprise Linux AI
- AI Inference Server concepts for performance and cost efficiency
- Packaging and exposing inference endpoints for applications
- Experiment tracking concepts using course exercises and tools
- Reducing and inspecting synthetic datasets for training efficiency
- Security and privacy considerations for enterprise LLM deployments
- Collaborative workflows between developers, data scientists, and SMEs
- Model evaluation at a high level for enterprise acceptance criteria
- Deployment patterns for on‑premises and cloud environments supported by RHEL AI images
- GPU and accelerator options overview for inference images
- Operational tasks for model serving and lifecycle management
- Responsible and efficient use of LLMs in production contexts
- Mapping course outcomes to Red Hat’s AI skills path
- How AI296 complements Developing and Deploying AI/ML Applications on OpenShift AI (AI267)
- Next steps for certification readiness with EX267
Terms & Conditions
The supply of this course/package/program is governed by our terms and conditions. Please read them carefully before enrolling, as enrolment is conditional on acceptance of these terms and conditions. Proposed course dates are given, course runs subject to availability and minimum registrations.
Frequently Asked Questions (FAQ's)
What does AI296 focus on compared with AI267?
AI296 teaches how to fine‑tune and serve LLMs using Red Hat Enterprise Linux AI and Granite models, while AI267 focuses on developing and deploying AI/ML applications on OpenShift AI. Many learners take AI296 before moving to OpenShift‑based workflows.
Does the course include hands‑on practice with data and models?
Yes. The course follows a practical approach to fine‑tuning and serving models, and exercises include working with curated or synthetic datasets to support experimentation.
Is prior OpenShift knowledge required for AI296?
No. AI296 is centered on Red Hat Enterprise Linux AI. OpenShift AI knowledge is helpful for later steps on the Red Hat AI skills path, but it is not a requirement for this course.
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.
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