Kubernetes and cloud-native engineer
Learn to run, secure and extend applications on Kubernetes, including AI workloads, using AI coding assistants from week 1, in 20 weeks.
Coming soon · planned, not written yet
- 20
- weeks planned
- ~40 h
- a week
- 5
- milestone projects
- 1
- project, grown every week
These are planned figures. The weeks are written after we check the tools against current job postings and official documentation, so the final route may differ.
Built for the AI era
How AI fits this roadmap
AI assistants now draft much of the YAML, Helm charts and policies that cluster engineers used to write by hand, and they can be wrong about fields, versions and permissions. This roadmap teaches you to use an assistant every week and to check its output against the cluster, the official docs, dry runs and policy checks. In the later weeks you also schedule GPU and batch AI workloads on Kubernetes, because running AI workloads is now a common reason to use a cluster.
The main project
ClusterKit
A small multi-service application with an AI-backed feature that you run on Kubernetes, deliver from Git, secure with policies, monitor, and extend with your own operator.
What you will be able to do
- 01
Deploy a multi-service application to a cluster with Deployments, Services, config, secrets and storage, and explain every object you applied.
- 02
Fix broken pods, failing rollouts and network problems from kubectl output, events and logs, including problems an AI assistant introduced.
- 03
Package the application with Helm and deliver it from a Git repository with Argo CD, with a tested rollback.
- 04
Enforce access rules and admission policies with RBAC and Kyverno, and review each AI-drafted manifest against them before it is applied.
- 05
Run a model-serving workload that requests GPUs or queued batch jobs, and watch it with Prometheus metrics and alerts.
01The route
Five months, one month at a time.
Each month ends in a milestone project, so you always have something to show for the work.
Month 1 · Weeks 1–4
Containers, Kubernetes basics and AI-assisted work
Containers, the Kubernetes API and kubectl, Pods, Deployments and Services on a local cluster. You set up an AI assistant, learn to prompt it for manifests, and check every field with dry runs and the official docs.
MilestoneClusterKit running on a local cluster from plain manifests, with a log of AI suggestions you accepted and rejected
Month 2 · Weeks 5–8
Configuration, storage, networking and troubleshooting
ConfigMaps, Secrets, volumes, probes, resource limits, Services, the Ingress API and the Gateway API, with a note on why the ingress-nginx controller was retired. You practise diagnosing broken workloads and test whether an AI explanation of a failure is actually the cause.
MilestoneA set of broken ClusterKit scenarios, each with a written and verified fix
Month 3 · Weeks 9–12
Packaging and Git-driven delivery
Helm charts, Kustomize overlays, environments, and delivery from Git with Argo CD, including rollbacks. You use AI to draft charts, then lint, render and diff them before they ship.
MilestoneEvery merge to Git updates the cluster, and a bad release is rolled back cleanly
Month 4 · Weeks 13–16
Cluster operations, security and observability
Cluster setup and upgrades, RBAC, network policies, admission policies with Kyverno, and metrics and alerts with Prometheus. You test AI-drafted RBAC and policies for being too open.
MilestoneA cluster with enforced policies, least-privilege access, dashboards and alerts around ClusterKit
Month 5 · Weeks 17–20
AI workloads, operators and capstone
Serve a model behind ClusterKit, request GPUs through device plugins, queue batch jobs with Kueue, and write a small operator with kubebuilder. Then a portfolio write-up and interview preparation.
MilestoneA model-serving workload, a working custom operator and a portfolio write-up
03What is inside
Everything a week comes with.
Every week is built the same way, so once you have done one you know how all of them work.
- 01
One project, ClusterKit
You grow one application on a cluster, with an AI feature inside it, so every tool lands in something real.
- 02
A day-by-day plan
Each week has a Learn part and a Do part, with a checklist so you know when you are done. Most weeks include a task to do with an AI assistant and a step to check its work.
- 03
A project in every milestone
Five milestones, each ending in something you can show and link to.
- 04
Reference topics
Short topic pages with check questions you can come back to.
- 05
A job track
Interview practice, portfolio and resume help, and an application plan.
- 06
Progress in your Study plan
Add weeks to your Study plan, give them dates, and finished days are counted for you.
04Is it for you
Who this is, and is not, for.
Better to know before you start than six weeks in.
It is for you if
- You can use Linux and the command line, and you have run a container before.
- You are a developer, system administrator or DevOps engineer who wants clusters to feel routine and wants to be strong with AI tools.
- You can give about 40 hours a week for five months.
It is not for you if
- You have never used a terminal or run a container. Do a beginner course first.
- You want a job guarantee. Nobody can promise that, and this roadmap does not.
- You want to apply AI-written manifests to a cluster without reading them. This roadmap trains you to check them.
- You want a broad cloud or full security career. Separate roadmaps cover those.
?Questions
Before you start
- Is this roadmap ready?
- Not yet. This is the plan. The weeks are being written and checked against current job postings and official documentation. Leave your email and we will write to you when it opens.
- What will it cost?
- The price is not set. Roadmaps are bought once, with no subscription. We will announce it when this one opens.
- Will the plan change?
- It may. Before the weeks are written we check the tools against current job postings, so the lengths and months here are planned, not final.
- Will it prepare me for the CKA or CKAD exam?
- It covers much of the same ground, including troubleshooting, networking, storage and cluster setup. It is not an exam course, and we will say clearly which exam topics are covered once the weeks are written.
- Will it get me a job?
- It will give you skills and proof of work. It cannot promise a job on a fixed date, because hiring depends on the market, your location and your past work.
Not open yet
Leave your email and we will write once, when it opens.