LLMOps: How LLMs Are Deployed and Scaled in Production

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 composite case built from common public patterns, and the course says so openly, with numbers that are illustrative rather than taken from one company. It starts on a hosted API, works in a pilot, and then runs into a projected bill of tens of thousands of dollars a month and answers that take six seconds at peak. From there you watch the team choose an open-weight model with an eval set of real tickets, serve it with vLLM on GPUs in Kubernetes, crash on memory in the first week, fix latency with batching, caching and quantization, add evals and guardrails before launch, watch the service with tracing and dashboards, and survive a traffic spike six times larger than normal.

Every new term appears exactly when the team needs it, so you learn it as the answer to a problem rather than as a definition. Diagrams drawn for the course show the layers, the timelines and the memory on the GPU, so you can see the mechanism instead of imagining it. Inside there are five modules.

Module 1 explains what LLMOps is, how it differs from classic MLOps, what the serving stack is made of, and how one request travels through it. Module 2 is the vocabulary of LLM deployment: TTFT, latency and throughput, KV cache and batching, quantization and model size, what inference servers such as vLLM, TGI and Triton actually do, and the cost trade-off between a hosted API and self-hosting. Module 3 is the heart of the course: the HelpDesk AI story in eight lessons, from the exploding API bill to the platform the team ended up with six months later.

Module 4 shows LLMOps in daily work: what an engineer's week looks like, how model and prompt changes ship safely through evals, canaries and rollbacks, and where the money goes in an LLM service along with the most common mistakes that waste it. Each module opens with a short key-terms lesson and closes with a printable memo you can keep at hand, plus an optional quiz of two or three questions that you can skip freely. The last module looks forward.

It describes the roles around an LLM in production, including the LLMOps or platform engineer, the applied ML engineer, the SRE, the platform team and the product owner, and it helps you see which of them fits your background and interests. It then names what this course deliberately skipped, such as fine-tuning, training at scale and step-by-step setup on a specific cloud, and it lays out a sensible order for going deeper if you decide this is your direction. You finish with a clear map of the field and a short list of the sources worth reading next, starting with the inference server documentation and the PagedAttention paper.

Be clear about what this course is not. It does not teach you to deploy, configure or debug a model with your own hands. There are no labs, no coding exercises and no homework.

You watch, you understand, and you come away able to reason about LLM serving, read an architecture diagram, ask the right questions in a design review and judge the trade-offs a team is making. If your goal is to type the commands yourself, take this course first for the picture, and then pick a hands-on course for the practice. Two lessons are open as free previews, the first key-terms lesson and the first lesson of the HelpDesk AI story, so you can see the style before you decide.