Red Hat AI Inference Fundamentals

Deploying AI models into production requires more than just training a good model; it demands a stable, scalable, and cost-effective infrastructure. This text-based course guides you through the core concepts of running high-performance artificial intelligence inference inside enterprise environments. You will learn how to transition models from development to production while maintaining low latency and high reliability. What you'll learn: Understand the foundational architecture of containerized AI inference engines; Deploy machine learning models on enterprise Kubernetes platforms; Optimize inference workloads to balance performance and hardware costs; Configure secure API endpoints for seamless model integration; Implement modern observability practices to monitor model drift and system health. The course begins with essential terminology and the basic architecture of modern inference engines, before guiding you through practical deployment configurations and scaling strategies. This course is designed for system administrators, DevOps engineers, and software developers who are new to enterprise AI infrastructure. Start building a robust foundation for scalable AI deployment today.