Course Overview
Experience the practices, culture, and tools that enable teams to reliably and efficiently build, deploy, and maintain GenAI applications in production.
Who should attend
This experience demonstrates how individuals across different roles must learn to share, collaborate, and work toward a common goal to achieve positive outcomes and drive generative AI innovation.
It is especially valuable for:
- AI Platform Users: AI engineers, application developers, data scientists, and data engineers building generative AI applications
- AI Platform Providers: ML/GenAIOps engineers and platform engineers deploying and managing AI infrastructure
- AII Platform Stakeholders: Architects and IT managers evaluating and overseeing generative AI adoption strategies
The scenario incorporates technical aspects of working with large language models and generative AI systems, offering practical insights into how these roles can align their efforts.
Prerequisites
- A chromium-based browser
- Containers, Kubernetes and Red Hat OpenShift Technical Overview (DO080) or basic understanding of OpenShift/Kubernetes and Containers is helpful
- Basic level understanding of AI or how your business can drive value from AI is beneficial
Course Objectives
Impact on the Organization
- Many organizations face operational complexity and tool sprawl across teams, prompt and config drift leading to inconsistent outputs, quality regressions that slip in with changes, unmanaged grounding causing hallucinations, safety risks from prompt injection and harmful content, and unpredictable latency and costs that block scale. GenAIOps addresses these challenges through standardization, treating prompts and configs as code, continuous automated evaluations, governed RAG, platform-enforced guardrails, and end-to-end observability.
- This course introduces real-world GenAIOps culture principles and modern practices. You will experience an AI-enabled application lifecycle end-to-end, from prompt and config versioning through deployment, continuous evaluation, and day-two operations. By the end of the course, you will be equipped to apply GenAIOps principles and leverage Red Hat AI Enterprise to drive and lead generative AI transformation initiatives within your organization.
Impact on the Individual
- As a result of attending this course, you will become familiar with the GenAI platform, understand where Red Hat AI Enterprise fits within the GenAIOps ecosystem, and experience an AI-enabled application lifecycle from end to end. You will gain practical patterns to build, ship, and run AI-enabled applications at scale, learning how to take them from prototype to production and keep them reliable.
Course Content
GenAIOps Enablement with Red Hat AI Enterprise (AI501) is a five-day immersive enablement, delivered the Red Hat Way, to build the skills that teams need to articulate and deliver on their AI vision. While many AI training programs focus on a particular framework or technology, this course covers how the tools fit together in a full Generative AI Operations workflow, treating the AI-enabled application, not just the model, as the unit of delivery.
To achieve the learning objectives, participants should include multiple roles from across the organization. AI engineers, application developers, platform engineers, architects, and IT managers will gain experience working beyond their traditional silos. The daily routine simulates a real-world delivery team building an AI-powered application, where cross-functional teams learn how collaboration breeds innovation. Armed with shared experiences and best practices, the team can apply what it has learned to help the organization's culture and mission succeed in the pursuit of generative AI initiatives.
This course is based on Red Hat AI Enterprise, including Red Hat OpenShift AI, as well as Red Hat OpenShift GitOps, Red Hat OpenShift Pipelines, and Generative AI models and open source libraries.
Course Content Summary
This course takes you on an end-to-end journey of an AI-enabled application, from prompt experimentation to production deployment, while bringing different personas together to collaborate on a single platform seamlessly.
- Understanding GenAI fundamentals, including tokens, context windows, and model behavior
- Experimenting with prompts and evaluating your first AI-enabled application
- Introducing an orchestration layer for standardized GenAI development
- Implementing Retrieval Augmented Generation (RAG) for knowledge-enhanced applications
- Building autonomous AI agents with tool-calling capabilities
- Deploying AI safety guardrails and implementing GenAI security practices
- Enabling observability with metrics, logging, and distributed tracing for GenAI systems
- Exploring small language models and multi-modal capabilities
- Optimizing models through quantization and compression techniques
- Implementing Models as a Service (MaaS) for scalable AI infrastructure