LLMOps: How LLMs Are Deployed and Scaled in Production

About this course

This course contains the use of artificial intelligence.

A chatbot demo takes an afternoon. Keeping that same model fast, affordable and trustworthy for thousands of real users is a different job, and it has a name: LLMOps. This course explains that job from end to end. After it, you understand what happens between the moment someone types a question and the moment the answer streams back, why that path costs what it costs, and what teams actually change when their LLM product becomes too slow, too expensive or too unpredictable. You will be able to follow a conversation about KV cache, continuous batching, quantization, time to first token, autoscaling, evals and guardrails, and you will know why each of them matters rather than just what the letters stand for. You will also see how the decisions fit together, so that a new tool or a new model on the market does not confuse you: you will know which layer it touches and which problem it is meant to solve.

The course is for people who meet large language models in production and want the whole picture in plain words. That includes software engineers who are about to work next to an LLM platform, DevOps and SRE people curious about GPU serving, data and ML engineers who have trained models but never served one at scale, and product managers, founders and analysts who sit in meetings where these words fly around and have to make decisions about cost and quality. No prior experience with machine learning is needed. If you know roughly what an API is, you have enough to start. The course is not for you if what you want is to sit at a terminal and deploy a model step by step with your own hands. Hands-on LLMOps courses and the documentation of the inference servers do that well, and this course points you to them at the end.

What makes this course different is one example carried from the first lesson to the last. You follow HelpDesk AI, a customer support assistant at a mid-size online retailer. It is a

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