LLMOps: Fine-Tuning, Evaluation, and Deployment at Scale

Large language models broke many assumptions of classical MLOps: they are enormous, expensive to train and serve, adapted more often by fine-tuning or prompting than trained from scratch, and notoriously hard to evaluate because their outputs are open-ended text rather than a label to score against. LLMOps is the discipline that adapts the MLOps lifecycle to these realities, covering how you specialise a foundation model, how you judge whether it is good, and how you serve it affordably at scale. The problem it solves is that the familiar tools, train a model, compute accuracy, deploy a small artifact, do not transfer cleanly: you rarely train an LLM from zero, accuracy does not capture whether a generated answer is correct, helpful, and safe, and serving a multi-billion-parameter model has unique latency and memory demands. LLMOps fills these gaps with parameter-efficient fine-tuning, LLM-specific evaluation methods, and specialised serving infrastructure. Understanding LLMOps matters because LLMs now power a large share of production AI, and operating them well, adapting cheaply, evaluating meaningfully, and serving efficiently, requires extending everything the course has covered to a class of models whose scale and open-ended behaviour demand new techniques.