Phase 1 · Weeks 1–4
Foundations — the machine stops being a mystery
Before any tools: what a server actually is, how to talk to one, and how professionals save their work. Four weeks that remove the fear.
Week 1How the internet serves an app. A server is a computer in a data centre that runs software for other people; "the cloud" is other people's computers, rented by the hour. You'll trace what happens when you open a website — and have your first conversation with a real machine in a terminal.
Week 2The Linux command line. Moving around a machine with typed commands instead of clicks: where am I, what's here, what's inside this file. The terminal is a conversation with the computer, not a test — and it answers every time.
Week 3Files, permissions and processes. Who is allowed to read, change or run each file (permissions), which programs the machine is running right now (processes), and how to start or stop them on purpose rather than by accident.
Week 4Git — a diary you can rewind. Every change to your work recorded, any earlier version recoverable, experiments kept safely separate. You'll make your first repository — the folder Git watches for you — and never lose work again.
By the end you can: navigate a server and not be scared of a terminal.
Phase 2 · Weeks 5–8
The core tools — package it, ship it, watch it
The four tools that appear in almost every DevOps job advert, each learned by using it on something real.
Week 5Containers. An app packed into its own box with everything it needs, so it runs identically on your laptop, a colleague's laptop and a server. You'll run one, stop one, and read what its exit code is telling you.
Week 6Images and registries. The recipe versus the meal: an image is the frozen blueprint, a container is that blueprint running. Registries are the shared shelves images are pulled from — the app-store idea, applied to server software.
Week 7Reading logs and basic monitoring. Logs are the app's diary of everything that happened — the first place engineers look when something breaks. Dashboards show the system's pulse, so you spot trouble before users do.
Week 8Your first automated pipeline. A conveyor belt that takes the code you saved, tests it, and ships it — no human touching each step. The industry calls it CI/CD; you'll call it the moment this all clicks together.
By the end you can: package an app and watch it run.
Phase 3 · Weeks 9–12
Production context — connect the tools under pressure
The last four weeks connect tool knowledge to investigation, cloud cost and the systems that serve AI. This is an introduction to the decisions involved, not proof of independent production readiness.
Week 9Kubernetes — where modern apps and AI models run. The shift supervisor for containers: it keeps the right number of copies running, replaces any that die, and spreads them across machines. You'll learn to read what it's telling you — the same skill whether the pods serve a web API or a model.
Week 10Handling a simple incident. The service is down and you're on shift. Observe first, follow the evidence, fix the cause, verify the fix. Practising the calm is the point — the skill every interviewer probes for, and exactly what a misbehaving AI system needs too.
Week 11Cloud fundamentals and cost — including the price of AI. Regions, virtual machines versus managed services, and why every choice carries a bill someone has to justify — GPUs and tokens most of all. Engineers who think about cost stand out in every hiring round.
Week 12DevOps for agentic AI — the differentiator. What AI agents are, how teams run and supervise them in production, and where AI-heavy systems fail — the direction the market is moving and the work fewer engineers can do. Then the capstone: walk through a real incident out loud, the way you would in an interview.
By the end you can: talk through a real incident in an interview.
Every topic above maps to free practice in the CloudCaive Labs — short daily questions with spaced repetition, and terminal simulations where the incidents happen to you, safely.