LLMOps Fundamentals: Operating Large Language Models in AI Systems

As large language models become central to modern software, deploying and maintaining them requires a completely new operational playbook. Traditional machine learning workflows fall short when dealing with the unique scale, cost, and unpredictability of generative AI. This text-only course guides you through the transition from classic MLOps to the specialized discipline of LLMOps. You will understand how to manage the lifecycle of foundation models, optimize their performance, and control operational costs using industry-standard methodologies. You will learn to: 1. Understand the core differences between traditional machine learning operations and LLMOps. 2. Explore the architecture of modern LLM applications, including Retrieval-Augmented Generation (RAG) and vector databases. 3. Analyze operational trade-offs between prompt engineering, fine-tuning, and using off-the-shelf foundation models. 4. Configure basic monitoring strategies for LLM latency, cost, token usage, and safety. 5. Evaluate LLM outputs using modern validation techniques and observability patterns. The course starts with fundamental terminology and the historical evolution of AI operations. You will then progress through conceptual frameworks for deployment, data ingestion pipelines, and continuous evaluation strategies, all explained through clear written guides and practical architectural walkthroughs. This course is designed for software engineers, data scientists, and technical product managers new to generative AI infrastructure. No prior experience with LLM deployment is required. Start reading today to build a solid foundation in the future of AI operations.