Build, automate, and maintain reliable software delivery pipelines and infrastructure.
You have reached the end of the beginner stages. Prove what you learned with a project.
You have reached the end of the intermediate stages. Prove what you learned with a project.
You have reached the end of the advanced stages. Prove what you learned with a project.
Package a simple web application into a Docker container and run it locally.
Write a script that pulls code, builds, and runs unit tests on each commit.
Deploy a multi-container application to a Kubernetes cluster with health checks.
Create Terraform scripts to provision a VPC, compute instances, and storage.
Set up a CI pipeline that runs linting, unit tests, and generates a build artifact.
Implement a GitOps workflow to manage infrastructure and application releases.
Build a Grafana dashboard that visualizes key metrics and logs from a live service.
Design and execute a chaos experiment that simulates node failure in production.
Analyze cloud usage and propose optimizations to reduce spend.
Create a simple ML model to predict failures and trigger alerts.
The same roadmap as a reading list: every stage, what each topic covers, and which ones are optional.
Core command line skills and system concepts essential for managing servers.
Fundamental concepts like IP, DNS, and routing that underpin cloud and on-premise environments.
Using Git for source code management, branching, and collaboration.
Automating tasks with Bash or PowerShell to improve efficiency.
Managing system state with tools such as Ansible or Chef.
Defining infrastructure declaratively using Terraform or CloudFormation.
Setting up CI pipelines to automatically build and test code.
Understanding Docker images, containers, and registries.
Writing robust scripts with error handling and modular design.
Deploying and managing containers with Kubernetes or Docker Swarm.
Implementing observability with Prometheus, Grafana, ELK, or similar tools.
Applying least-privilege, secrets management, and vulnerability scanning in pipelines.
Managing infrastructure and applications declaratively via Git repositories.
Understanding traffic management, security, and observability with Istio or Linkerd.
Deploying functions without managing servers using cloud provider services.
Modules, workspaces, and policy as code for large-scale environments.
Applying SLOs, error budgets, and incident response processes.
Introducing controlled failures to test system resilience.
Analyzing and optimizing system and application performance at scale.
Forecasting resource needs and cost-effective scaling strategies.
Fostering DevOps mindset, communication, and cross-functional teamwork.
Understanding regulatory requirements and audit practices across regions.
Monitoring cloud spend and implementing efficient resource usage.
Building a portfolio, networking, and pursuing certifications where applicable.
Staying current with new CI/CD platforms, IaC languages, and observability solutions.
Contributing to open source projects and attending industry events.
Implementing zero-trust architectures and advanced threat modeling.
Leveraging machine learning to predict incidents and automate remediation.
Design and implement an end-to-end pipeline for a multi-service application.
Plan and execute a move from on-premise to cloud using IaC.
Build a unified monitoring, logging, and alerting system for production workloads.
Create and run controlled failure experiments to improve resilience.
Typically 6-12 months of consistent part-time study to reach an intermediate level, longer for advanced mastery.
A formal degree is not required; practical experience and a solid portfolio are most important.
Common options include AWS/Azure/GCP cloud certifications, Certified Kubernetes Administrator, and HashiCorp Terraform Associate, but they vary by region.
Yes, many DevOps engineers start with system administration or software development backgrounds.
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