DevOps & Cloud Engineering — AWS & GCP
Automate everything: containers, pipelines, infrastructure as code and production reliability.
Lifetime access · Certificate included · US timezone sessions
The 30-minute demo is free, needs no card, and carries no obligation. If we aren't the right fit for your goals, we'll tell you.
- Tutorials finished, but nothing deployed you can show
- A resume that lists coursework instead of shipped work
- Freezing when an interviewer asks you to explain a decision
- Unsure which skills US employers actually screen for
- 4 deployed projects with real source code
- A US-format resume built around what you shipped
- Mock interviews with written feedback, before the real one
- A clear path toward DevOps Engineer
Who this course is for
This course was designed around specific situations. If one of these sounds like you, you're in the right place.
What you'll learn
By the end of this course you will be able to do each of the following in real projects, not just in exercises.
Containerize applications and orchestrate them with Kubernetes
Define reproducible infrastructure with Terraform
Build CI/CD pipelines with automated testing and safe rollbacks
Design for availability, scaling and cost efficiency
Instrument systems with metrics, logs, traces and alerting
Your learning roadmap
The course runs in phases. Each one ends with a concrete milestone, so you always know whether you're on track.
- 1
Linux, Containers & Networking
Weeks 1–4Focus: The substrate
What you'll do
- Work fluently with Linux processes, permissions and systemd
- Write efficient multi-stage Dockerfiles
- Understand container networking and storage
- Compose multi-service local environments
MilestoneA containerized multi-service application running locally with persistence.
- 2
Cloud Platforms & IaC
Weeks 5–8Focus: Infrastructure as code
What you'll do
- Provision compute, storage, networking and IAM on AWS and GCP
- Write modular Terraform with remote state and workspaces
- Design VPCs, subnets, security groups and load balancers
- Apply least-privilege IAM correctly
MilestoneA complete environment provisioned from scratch by Terraform alone.
- 3
Kubernetes & CI/CD
Weeks 9–11Focus: Orchestration and delivery
What you'll do
- Deploy and manage workloads with Deployments, Services and Ingress
- Handle configuration, secrets and persistent volumes
- Build pipelines with automated tests, scanning and staged rollouts
- Implement blue-green and canary deployments with rollback
MilestoneAn automated pipeline deploying to Kubernetes with zero-downtime releases.
- 4
Observability & Reliability
Week 12Focus: Running it in production
What you'll do
- Instrument metrics, logs and distributed traces
- Define SLIs, SLOs and meaningful alerts
- Practise incident response and write postmortems
- Optimise cloud cost without degrading reliability
MilestoneA monitored system with dashboards, alerts and a documented runbook.
Everything you'll cover
A map of the whole curriculum at a glance — every major area and the topics inside it.
- Docker
- Multi-stage builds
- Registries
- Compose
- Security scanning
- Pods & Deployments
- Services & Ingress
- ConfigMaps & Secrets
- Helm
- Autoscaling
- Terraform
- Modules
- Remote state
- Drift detection
- Policy
- AWS core services
- GCP core services
- VPC & networking
- IAM
- Cost management
- GitHub Actions
- Jenkins
- Testing gates
- Canary/blue-green
- Rollbacks
- Prometheus
- Grafana
- Logging
- Tracing
- SLOs
- Incident response
Week-by-week syllabus
A structured breakdown of exactly what you'll cover, week by week.
Projects you'll build
You finish with a portfolio of deployed work — the thing hiring managers actually look at.
Containerized Microservices Stack
Multi-service application with Docker Compose, persistence and health checks.
Terraform Cloud Environment
Fully reproducible VPC, compute, database and IAM setup on AWS and GCP.
Kubernetes Deployment Pipeline
Automated CI/CD deploying to a cluster with canary releases and rollback.
Observability Capstone
Metrics, logs, traces, dashboards, alerts and an incident runbook for a live system.
Where this course can take you
Typical roles this course prepares you for, with current US market compensation ranges.
| Role | US salary range | Demand |
|---|---|---|
| DevOps Engineer | $115,000 – $170,000 | Very high |
| Site Reliability Engineer | $130,000 – $190,000 | Very high |
| Cloud Engineer | $110,000 – $160,000 | Very high |
| Platform Engineer | $125,000 – $180,000 | Growing fast |
Salary ranges are indicative market figures for reference, not guarantees of employment or compensation.
Prerequisites
- Comfortable on the Linux command line
- Understanding of networking basics: DNS, HTTP, ports, TLS
- Some programming or scripting experience (any language)
Tools you'll use
Frequently asked questions
AWS or GCP — which should I focus on?
AWS has the largest market share and the most US job postings, so it gets the most weight in this course. GCP is covered alongside it because the underlying concepts are identical and multi-cloud fluency is increasingly expected. Once you understand one deeply, the second takes weeks, not months.
Do I need to know how to code for DevOps?
You need scripting, not application development. Comfort with Bash plus one language such as Python or Go is enough. Most DevOps work is automation, configuration and troubleshooting rather than building features.
Is Kubernetes too complex for a first DevOps job?
Kubernetes is complex, but it is also in most DevOps job descriptions. The course builds up from containers so the abstractions make sense rather than appearing as magic. You will not master every feature in three weeks, but you will be able to deploy, debug and operate real workloads.
Will this prepare me for AWS certification?
It covers substantial overlap with Solutions Architect Associate and DevOps Engineer Professional content, but it is a practical course, not an exam-cram. Students frequently take a certification afterward with limited additional study.
Why are DevOps salaries so high?
The role sits at the intersection of software, systems and operations, and mistakes are expensive and highly visible. The combination of breadth, on-call responsibility and direct impact on reliability and cloud spend keeps compensation among the highest in engineering.