Experience the practices, culture, and platform capabilities required to build, deploy, operate, and maintain Generative AI applications reliably in production.
GenAIOps Enablement with Red Hat AI Enterprise (AI501) is a five-day immersive program designed to help teams develop the skills required to turn a Generative AI vision into reliable, production-ready applications.
While many AI training programs focus primarily on a specific framework, model, or technology, AI501 takes an end-to-end Generative AI Operations approach. The course treats the entire AI-enabled application—not only the underlying model—as the unit of delivery.
Participants work through the full lifecycle of a GenAI application, from prompt experimentation and evaluation to deployment, observability, continuous improvement, and Day-2 operations.
The experience is deliberately cross-functional. AI engineers, application developers, platform engineers, architects, and IT managers work beyond their traditional roles and collaborate as a shared delivery team.
The daily workflow reflects a real-world environment in which multiple disciplines contribute to a single AI-powered application. Participants experience how shared practices, common tooling, and collaboration can improve consistency and support innovation across Generative AI initiatives.
The course is based on Red Hat AI Enterprise, including Red Hat OpenShift AI, Red Hat OpenShift GitOps, Red Hat OpenShift Pipelines, Generative AI models, and open source libraries.
Benefits for Organizations
Organizations adopting Generative AI often encounter operational complexity caused by tool sprawl, prompt and configuration drift, quality regressions introduced through change, poorly governed grounding that increases hallucination risk, security concerns such as prompt injection and harmful content, and unpredictable latency or costs that make scaling difficult.
GenAIOps provides a structured approach to addressing these challenges.
The practices introduced in AI501 can help organizations:
- Manage prompts and configurations as code.
- Apply version control to GenAI application changes.
- Introduce continuous automated evaluation.
- Govern RAG implementations more consistently.
- Enforce safety guardrails through the platform.
- Establish end-to-end observability.
- Move GenAI applications from prototype to production in a more controlled manner.
The course provides practical exposure to the full lifecycle of an AI-enabled application, from prompt and configuration versioning to deployment, continuous evaluation, and Day-2 operations.
Benefits for Participants
After completing AI501, participants should have a stronger understanding of the GenAI platform landscape and how Red Hat AI Enterprise fits into the broader GenAIOps ecosystem.
Participants will gain practical experience to:
- Understand the end-to-end lifecycle of a GenAI-enabled application.
- Establish structured prompt development and evaluation processes.
- Build RAG-based AI applications.
- Develop AI agents with tool-calling capabilities.
- Apply security controls and safety guardrails.
- Use metrics, logs, and distributed tracing for GenAI observability.
- Evaluate model optimization approaches.
- Design scalable model delivery with Models as a Service.
- Move AI applications from prototype to production.
- Apply GenAIOps practices to improve reliability and maintainability at scale.
Recommended Next Courses
After AI501, participants may continue with:
- MLOps Practices with Red Hat OpenShift AI (AI500) – Recommended for teams also working with predictive AI and machine learning models,
- Red Hat OpenShift Administration II: Configuring a Production Cluster (DO280) – Recommended for platform engineers who want deeper OpenShift administration skills.
