LLMOps: High-Throughput Serving with vLLM and LangChain

Deploying and managing open-source large language models in production requires efficient serving engines and structured application logic. This text-based course teaches you how to streamline language model operations using vLLM for high-throughput inference alongside LangChain for pipeline orchestration. You will gain practical understanding of modern LLMOps principles, learning how to configure inference engines, optimize memory usage, and connect served models to rich application workflows through written explanations and structured code walkthroughs. What you'll learn: - Understand core LLMOps concepts, terminology, and model lifecycle management - Configure and run high-performance model serving using vLLM and PagedAttention - Integrate self-hosted vLLM endpoints seamlessly into LangChain workflows - Build structured prompt chains, agents, and retrieval-augmented generation (RAG) pipelines - Apply modern optimization techniques for latency, throughput, and GPU memory management - Monitor model health, token usage, and system performance in production environments The course begins with foundational concepts in model hosting and orchestration before progressing to hands-on configuration, chain construction, and operational best practices. Each concept is reinforced with practical code snippets and clear written explanations. This course is designed for developers, data engineers, and AI practitioners who want to learn how to serve and orchestrate their own models without relying exclusively on closed proprietary APIs. Familiarity with basic Python is helpful, but no prior LLMOps or deployment experience is required. Start reading today to build robust, scalable language model architectures.