LLMOps Foundations: Managing Large Language Models in Production

Deploying large language models requires much more than just writing a good prompt. To build reliable, cost-effective, and scalable AI applications, you need to understand the specialized operational discipline known as LLMOps. This text-based course guides you through the foundational concepts of LLMOps, helping you transition from basic prototyping to production-ready AI systems. You will understand how to manage the unique lifecycle of generative AI, balance performance with API costs, and ensure outputs remain accurate and safe. What you'll learn: - Understand the core differences between traditional MLOps and LLMOps. - Analyze the trade-offs between model size, latency, API costs, and output quality. - Explore modern architectures including Retrieval-Augmented Generation (RAG) and vector databases. - Implement evaluation frameworks to measure correctness and mitigate model hallucinations. - Track and version prompts, model configurations, and system inputs systematically. - Monitor performance and security guardrails in live production environments. You will start with the fundamental definitions and the necessity of LLMOps before progressing to practical operational strategies, system design patterns, and evaluation workflows through clear written explanations and conceptual exercises. This course is designed for software developers, product managers, and data professionals who are new to LLM operations and want to build a solid conceptual foundation without needing advanced machine learning experience. Begin your journey into modern AI operations and learn how to manage generative models at scale today.