LLM Infrastructure & MLOps

Who this course is for

If you can already call an LLM API and deploy a basic cloud service, but you've never had to actually run a model yourself — pick a serving engine, decide how to quantize it, figure out why your GPU bill is three times what you budgeted — this is the infrastructure layer sitting underneath every real AI product. It builds on the AI Fundamentals and Cloud Engineer roadmaps, so you should be comfortable with both before starting.

What you'll actually build

The course moves through five infrastructure problems in order: choosing and configuring a model serving architecture (vLLM, TGI, and the batching strategies that determine throughput), fine-tuning and quantizing open-source models with techniques like LoRA/QLoRA and GGUF so they run efficiently outside a hosted API, building evaluation pipelines that catch model regressions before your users do, managing GPU infrastructure cost and scaling decisions, and instrumenting the whole pipeline for observability. Every module pairs a concepts lesson with a hands-on build against the same running system.

The capstone

You finish by standing up a self-hosted LLM serving stack complete with monitoring and cost tracking — not a notebook demo, but the kind of infrastructure project you'd actually be handed on the job, and one you can walk an interviewer through end to end.

Why this matters right now

Most AI engineers can call an API; far fewer can actually operate the infrastructure behind one. That gap is exactly where this course sits, and it's a large part of why LLM infrastructure and MLOps roles carry a premium over general AI engineering roles — the constraint isn't model access anymore, it's people who can serve, fine-tune, and run these systems reliably at a cost that makes sense.