Red Hat AI Inference Server Technical Overview

Deploying AI models in production requires robust infrastructure that can handle heavy workloads while keeping operational costs low. This text-based course introduces you to the core architecture and features of the Red Hat AI Inference Server, showing you how to optimize model serving in enterprise environments. You will learn how to manage model deployment pipelines, leverage hardware acceleration, and implement modern practices like retrieval-augmented generation (RAG) and vector database integration. What you'll learn: - Understand the foundational architecture of enterprise AI inference servers - Configure and optimize AI workloads to maximize hardware utilization and reduce costs - Deploy and scale machine learning models using modern containerized workflows - Integrate inference engines with vector databases for retrieval-augmented generation patterns - Implement monitoring and basic observability for production model serving Starting with key terminology and foundational concepts, the course guides you through practical configuration strategies and deployment patterns. You will read through real-world scenarios, complete written exercises, and analyze configuration snippets designed to build your confidence in managing AI infrastructure. This course is designed for system administrators, DevOps engineers, and IT professionals who are new to AI model serving and want to build a solid foundation in modern inference infrastructure. No prior experience with AI deployment is required.