Generative AI is rapidly changing how modern applications are built, and Large Language Models (LLMs) are becoming an important part of enterprise AI infrastructure. This course is designed to give you a practical, hands-on understanding of deploying and serving LLMs using Red Hat OpenShift AI. The course starts with the fundamentals of Large Language Models, LLM inference, model runtimes, model selection, and private or sovereign AI architectures. You will understand what happens behind the scenes when a user sends a prompt to an AI model and how an end-to-end LLM application can be built using your own infrastructure.
You will then move into the OpenShift AI platform, where you will learn how to prepare the infrastructure, install the OpenShift AI Operator, configure a Data Science Cluster, and understand important AI platform components such as KServe, Workbench, Dashboard, Model Registry, and AI pipelines. The course also covers LLM model selection and optimization, including model parameters, memory requirements, model precision, and quantization. You will understand why different model formats require different infrastructure resources and how quantized models can help reduce memory requirements.
