Let’s make it work for you
Overview
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.
Prerequisites
Participants should have:
- 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
Target audience
This course is designed 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.
Delegates will learn how to
By the end of this course, learners will be able to:
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
- Audience for this 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.
Exams and assessments
There are no examinations or certifications associated with this course.
Hands-on learning
This course is driven by a series of Simulated AI development scenarios
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