Ultimate LLMOps Bootcamp - Production LLM Systems

Most LLM courses stop where production work starts.

You get a prompt that returns a good answer, and then the hard questions begin. Is this change actually better, or does it just read better? What blocks a release? How do you package a model so someone else can verify exactly what shipped? When the assistant gets slow, or expensive, or wrong, how do you find out before your users tell you?

This bootcamp answers those questions by building one system and operating it the whole way through.

You build one assistant, called OpsMate, and take it through the complete LLMOps lifecycle.

You start with what a model actually does — tokens, prefill and decode, the KV cache, quantization — and why those decide your latency, memory and bill. You serve it behind an OpenAI-compatible API. You give it your own documents with RAG. You build a golden set and record a baseline, so "better" becomes a number instead of an opinion. You fine-tune with LoRA and then let the evaluation gate decide whether it ships. You package and sign the model as a versioned artifact, deploy it to Kubernetes, canary it, and promote it on quality evidence rather than on a green deploy. You add observability, autoscaling, GitOps, a gateway with budgets and guardrails, and finally a bounded tool-calling agent.

The labs are built around evidence, not around everything working.

You will watch a weak prompt lose an A/B test. You will watch a fine-tuned model fail its quality gate and get blocked — which is the gate doing its job. You will hit the promotion lie, where the tag moves and the bytes do not. You will apply a bad liveness probe on purpose and watch it turn a slow model load into a restart loop. Every one of those is a real failure from building this course, and each one teaches a decision you will have to make for real.

Everything required runs on an average 8 GB laptop.

No GPU. No cloud account. No paid API. No Docker Desktop subscription. The whole course uses open models and local infrastructure, and every lab tells you its resource path up front. Kubernetes shows up later as one deployment environment, taught on a local cluster — this is a Production LLMOps course, not a Kubernetes administration course.

What you walk away with

A working system you built yourself, and a repeatable method for the question that actually matters in this job: is this model change ready to promote, and what is my evidence?