Efficient AI Inference with Red Hat AI

Deploying machine learning models is only half the battle; the real challenge lies in serving those models quickly and cost-effectively at scale. Understanding how to optimize inference workloads ensures your applications remain responsive under heavy real-world demands. In this practical text-only course, you will master the fundamentals of model serving and learn how to leverage Red Hat AI to streamline your deployment pipeline. You will transition from basic concepts to advanced optimization strategies, ensuring your AI systems perform reliably in production environments. What you'll learn: - Understand the core concepts of AI inference, latency, throughput, and hardware acceleration. - Configure Red Hat AI environments to serve models efficiently at scale. - Apply optimization techniques such as quantization to reduce model footprint. - Implement modern MLOps practices for seamless model deployment and monitoring. - Scale inference workloads dynamically using containerized orchestration patterns. - Explore retrieval-augmented generation (RAG) patterns for optimized contextual querying. We begin with essential terminology and the foundational mechanics of model serving. From there, you will read through clear, structured explanations and code snippets that illustrate how to configure, optimize, and maintain high-performance inference pipelines. This course is ideal for beginners, developers, and system administrators looking to enter the field of AI deployment. No prior machine learning serving experience is required. Start building faster, more efficient AI deployment pipelines today.