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GenAIOps Enablement with Red Hat AI Enterprise (AI501)

AI501 - Experience the practices, culture, and tools that enable teams to reliably and efficiently build, deploy, and maintain GenAI applications in production.

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.

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.

Note: This course is only delivered as classroom-based sessions and closed course options.

There is no RHLS-course for this reference. Customer can get access to the e-learning course content by purchasing a RedHat RHLS Standard subscription (access to the entire RedHat E-learning offering).

Updated July2026

GK# 832033 Vendor# AI501
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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.

What You'll Learn

After this course participants should be able to:

  • Understand GenAI fundamentals, including tokens, context windows, and model behavior.
  • Experiment with prompts and evaluating your first AI-enabled application.
  • Introduce an orchestration layer for standardized GenAI development.
  • Implement Retrieval Augmented Generation (RAG) for knowledge-enhanced applications.
  • Build autonomous AI agents with tool-calling capabilities.
  • Deploy AI safety guardrails and implementing GenAI security practices.
  • Enable observability with metrics, logging, and distributed tracing for GenAI systems.
  • Explore small language models and multi-modal capabilities.
  • Optimize models through quantization and compression techniques.
  • Implement Models as a Service (MaaS) for scalable AI infrastructure.

Course Outline

Core Foundations

  • GenAI Fundamentals

Explore what GenAIOps is and how large language models work, including tokenization, context windows, and the factors that affect model behavior and performance.

  • Experimenting with Prompts

Learn to craft effective prompts using system prompts and user prompts, configure temperature and output parameters, and optimize prompts for specific use cases.

  • Evaluating Your First AI-Enabled Application

Implement prompt versioning, build evaluation pipelines, automate testing, and measure application quality systematically.

  • Introducing the Orchestration Layer

Introduce an orchestration layer for building GenAI applications, deploy backend services, and implement GitOps practices for continuous deployment.

Advanced Topics

  • Integration and Orchestration

Deploy vector databases, build RAG pipelines for knowledge-enhanced applications, implement tool calling, and create autonomous AI agents.

  • Safety and Observability

Deploy AI safety guardrails, implement GenAI security practices, enable the three pillars of observability, metrics, logs, and traces.

  • Modeling Techniques

Explore small language models for efficient deployment and multi-modal model capabilities for handling diverse input types.

  • Optimization and Deployment

Apply quantization and compression techniques for improved performance, explore fine-tuning approaches, implement Models as a Service (MaaS), and bring it all together in a production deployment.

Labs Outline

Labs are provided by RedHat for this course.

Prerequisites
  • A chromium-based browser.
  • Free module “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.

Confirmation of the correct skill set knowledge can be obtained by passing the online skills assessment at Red Hat Skills Assessment.

Follow-On Courses

  • MLOps Practices with Red Hat OpenShift AI (AI500) for teams also working with predictive AI and machine learning models.
  • Red Hat OpenShift Administration II: Configuring a Production Cluster (DO280) for platform engineers seeking deeper OpenShift expertise.