DevOps Engineer – Complete Roadmap

A skilled DevOps Engineer can build, deploy, monitor, secure, and scale software systems reliably in cloud or hybrid environments.

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DevOps Engineer – Complete Roadmap
DevOps Engineer – Complete Roadmap
1. Understanding DevOps
Dev + Ops culture
Automation & collaboration
Continuous improvement
CI & CD
Infrastructure as Code (IaC)
Monitoring & feedback loops
Plan → Code → Build
Test → Release → Deploy
Operate → Monitor
2. Prerequisites & Foundations
Linux basics
Users, permissions, services
Shell scripting (bash/zsh)
Networking (TCP/IP, DNS, DHCP)
Subnets & NAT
Firewalls & load balancers
HTTP/HTTPS & SSL/TLS
Reverse proxy (Nginx/HAProxy)
Bash & Python scripting
YAML & JSON configs
Git fundamentals
GitHub / GitLab / Bitbucket
Branching strategies
3. Continuous Integration (CI)
Automated builds
Unit & integration tests
Trigger on commits/PRs
Jenkins
GitHub Actions
GitLab CI / CircleCI
Travis CI
SonarQube (code quality)
Nexus / Artifactory
Pipeline: pull → build → test
Reports & artifacts
4. Continuous Delivery & Deployment
Blue–Green deployments
Canary releases
Rolling updates
Zero-downtime deploys
Jenkins pipelines
Argo CD
Spinnaker
GitLab CD / Harness
Docker images as artifacts
Auto-deploy to Kubernetes
5. Containers & Virtualization
Docker basics
Images & containers
Volumes & networks
Dockerfile best practices
Docker Compose
Private registries
Health checks & logs
Env variables & secrets
VM basics (KVM, VMware)
Containers vs VMs
6. Kubernetes (K8s) Orchestration
Pods & Deployments
ReplicaSets & Services
ConfigMaps & Secrets
Namespaces & Ingress
Volumes & PVCs
Helm charts
HPA & autoscaling
kubectl / minikube / kind
Lens / K9s
RBAC & network policies
CRDs & operators
Service Mesh (Istio/Linkerd)
7. Infrastructure as Code (IaC)
Terraform
CloudFormation
Pulumi
Ansible
Chef / Puppet / SaltStack
Resources & providers
Variables & state
Remote backend
Modules & reuse
Ansible playbooks & roles
Idempotent automation
8. Cloud Platforms
AWS: EC2, S3, RDS
Lambda & CloudFormation
CloudWatch & IAM
Azure: VMs, Blob, AKS
Azure Pipelines & Functions
GCP: GCE, GKE, Storage
Cloud Functions & Pub/Sub
Other clouds (DigitalOcean, etc.)
9. Monitoring, Logging & Observability
Prometheus metrics
Grafana dashboards
Datadog / New Relic
Dynatrace
ELK (Elasticsearch, Logstash, Kibana)
EFK (Fluentd/FluentBit)
Graylog / Splunk
OpenTelemetry
Tracing with Jaeger / Zipkin
10. Security in DevOps (DevSecOps)
Shift-left security
Continuous vuln scanning
Policy as code
Snyk / Trivy / Clair
Aqua / Anchore / Prisma
Vault for secrets
OPA (Open Policy Agent)
Falco runtime security
SAST & DAST
Infra security scans
CIS & compliance checks
11. MLOps Basics
Data & model versioning
Training & validation
Serving & monitoring
MLflow
DVC
Kubeflow
TFX
Airflow / Prefect
Weights & Biases
TensorFlow Serving / TorchServe
BentoML
FastAPI / Flask APIs
12. CI/CD – DevOps, DevSecOps & MLOps
DevOps: build–test–deploy
Jenkins, GitHub Actions, Argo CD
DevSecOps: secure pipelines
Snyk, Trivy, Vault, SonarQube
MLOps: model CI/CD
MLflow, Kubeflow, Airflow
13. Advanced Automation & Scripting
Server setup with Ansible
Automated Docker builds
Jenkins Groovy pipelines
Bash/Python for logs & backups
Cron jobs
Integrate tools via REST APIs
14. Collaboration & Agile Practices
Scrum / Kanban
Jira / Trello / ClickUp
Confluence / Notion docs
Slack / Teams comms
Pull requests & reviews
Branch policies
15. Common DevOps Toolchain (2025)
Git / GitHub / GitLab
Jenkins / GitLab CI / Argo CD
Docker / Podman
Kubernetes / Helm / Kustomize
Terraform & Ansible
Prometheus & Grafana
ELK / EFK
Vault / Trivy / Snyk
AWS / Azure / GCP
MLflow / Kubeflow / Airflow
16. Real-World DevOps Projects
CI/CD for microservices
Jenkins + Docker + K8s + Helm
IaC on AWS
Terraform + Ansible + EC2/S3
Monitoring stack
Prometheus + Grafana
Secure image pipeline
Docker + Trivy + Vault
MLOps demo project
MLflow + Airflow + K8s
DevSecOps pipeline
Scan → build → test → deploy
17. Advanced DevOps Concepts
GitOps (Argo CD)
Chaos Engineering
Gremlin / Litmus
Blue/Green & Canary rollouts
Argo Rollouts
Serverless DevOps
AWS Lambda / Cloud Functions
Policy as Code (OPA/Kyverno)
Edge & FinOps basics
18. Certifications for Growth
AWS DevOps Engineer Pro
Azure DevOps Engineer
GCP DevOps Engineer
Terraform Associate
Docker Certified Associate
CKA / CKAD
Certified Jenkins Engineer
DevSecOps Foundation
19. Soft Skills & Mindset
Systems thinking
Troubleshooting under pressure
Team collaboration
Clear communication
Good documentation
Continuous learning
Kaizen mindset
20. Effort & Timeline to Expertise
Foundations: 3–4 months
CI/CD & Docker: 4–5 months
IaC & Cloud: 5–6 months
Security & monitoring: 4 months
MLOps & advanced: 3–4 months
Total: 18–24 months
20–25 hrs/week practice
3–5 full DevOps projects
Hands-on cloud & labs

Complete Roadmap

1. Understanding DevOps

What is DevOps?

DevOps is a combination of Development (Dev) and Operations (Ops) that promotes:

  • Continuous Integration (CI)
  • Continuous Delivery (CD)
  • Infrastructure as Code (IaC)
  • Automation and Monitoring
  • Collaboration between teams

DevOps Lifecycle Phases

  • Plan – project & version control
  • Code – source management
  • Build – CI pipelines
  • Test – automated testing
  • Release – CD pipelines
  • Deploy – container orchestration
  • Operate – infrastructure monitoring
  • Monitor – feedback loops

2. Prerequisites & Foundations

Operating Systems

Linux (Primary OS for servers)

  • File system, permissions, users/groups
  • Shell scripting (bash, zsh)
  • System services (systemctl, journalctl)
  • Network utilities (curl, netstat, ss, nslookup)
  • Package managers (apt, yum, dnf)

Networking Concepts

  • TCP/IP, DNS, DHCP, Subnetting, NAT
  • Ports, Firewalls, Load Balancing
  • VPN, Reverse Proxy (Nginx / HAProxy)
  • HTTP/HTTPS, SSL/TLS Certificates

Scripting Languages

  • Bash / Shell scripting
  • Python (automation & tools)
  • YAML / JSON (configuration & data representation)

Version Control

  • Git (branching, merging, tagging)
  • GitHub / GitLab / Bitbucket
  • Git Flow / Trunk-based development

3. Continuous Integration (CI)

CI ensures that code from all developers is integrated and tested automatically.

Core Concepts

  • Build automation
  • Unit and integration testing
  • Automated triggers on commits/pull requests

Tools

  • Jenkins (most used in enterprises)
  • GitHub Actions / GitLab CI / CircleCI / TravisCI
  • SonarQube – code quality scanning
  • Nexus / JFrog Artifactory – artifact repositories

CI Pipeline Example

  • Pull code from Git
  • Build using Maven/Gradle
  • Run tests
  • Generate reports
  • Push artifacts to repository

4. Continuous Delivery & Deployment (CD)

Automate the release process to move code from staging to production.

Core Concepts

  • Blue-Green Deployment
  • Canary Deployment
  • Rolling Updates
  • Zero-Downtime Deployment

CD Tools

  • Jenkins (with pipeline scripts)
  • Argo CD
  • Spinnaker
  • GitLab CD
  • Harness

Containerization Integration

  • Docker images as build artifacts
  • Automated container deployment to Kubernetes
  •  

5. Containers & Virtualization

Containers allow you to package and ship applications consistently across environments.

Docker

  • Images, Containers, Volumes, Networks
  • Dockerfile creation & optimization
  • Docker Compose (multi-container apps)
  • Private Docker Registry

Container Management

  • Image versioning and tagging
  • Environment variable injection
  • Health checks and logging

Virtualization (optional background)

  • VMware, VirtualBox
  • Hypervisors (KVM, Xen)

6. Container Orchestration – Kubernetes (K8s)

Kubernetes automates deployment, scaling, and management of containerized applications.

Core Concepts

  • Pods, ReplicaSets, Deployments, Services
  • ConfigMaps, Secrets, Namespaces
  • Ingress Controllers
  • Volumes and Persistent Storage
  • Helm Charts (K8s package manager)
  • Horizontal & Vertical Pod Autoscaling

Tools

  • kubectl, minikube, k3s, kind
  • Lens – K8s visual dashboard
  • K9s – CLI cluster management

Advanced Topics

  • RBAC (Role-Based Access Control)
  • Network policies
  • Custom Resource Definitions (CRDs)
  • Service Meshes: Istio / Linkerd

7. Infrastructure as Code (IaC)

IaC automates infrastructure provisioning through code.

Tools

  • Terraform – cloud-agnostic IaC tool
  • AWS CloudFormation (AWS only)
  • Pulumi (IaC with programming languages)
  • Ansible – configuration management
  • Chef / Puppet / SaltStack (older tools)

Terraform Essentials

  • Providers, resources, variables, state files
  • Remote backends and workspaces
  • Modules and reusable templates

Ansible Essentials

  • Playbooks (YAML)
  • Roles, inventories, variables
  • Idempotency & SSH-based automation

8. Cloud Platforms (Core to DevOps)

A DevOps Engineer must be comfortable with at least one major cloud platform.

Amazon Web Services (AWS)

  • EC2, S3, RDS, Lambda, CloudFormation
  • Elastic Beanstalk, CloudFront, IAM
  • CloudWatch for monitoring

Microsoft Azure

  • Virtual Machines, Blob Storage
  • Azure Pipelines, AKS, Functions

Google Cloud Platform (GCP)

  • Compute Engine, Cloud Storage, GKE
  • Cloud Functions, Pub/Sub

Others (optional)

  • DigitalOcean, Linode, Vercel, Render

9. Monitoring, Logging & Observability

Monitoring Tools

  • Prometheus – metrics collection
  • Grafana – dashboard visualization
  • Datadog / New Relic / Dynatrace – SaaS monitoring

Logging Tools

  • ELK Stack (Elasticsearch, Logstash, Kibana)
  • EFK Stack (Fluentd/FluentBit, Elasticsearch, Kibana)
  • Graylog / Splunk

Tracing

  • OpenTelemetry
  • Jaeger / Zipkin

10. Security in DevOps → DevSecOps

DevSecOps integrates security into every phase of DevOps.

Key Principles

  • “Shift Left” security (scan early in pipeline)
  • Continuous vulnerability detection
  • Automated security policies

Tools

  • Snyk / Trivy / Clair – image scanning
  • Aqua Security / Anchore / Prisma Cloud – runtime security
  • Vault (by HashiCorp) – secrets management
  • Open Policy Agent (OPA) – policy as code
  • Falco – runtime threat detection

Practices

  • Static Application Security Testing (SAST)
  • Dynamic Application Security Testing (DAST)
  • Infrastructure Security Scans
  • Compliance checks (CIS Benchmarks, ISO 27001)

11. MLOps (Machine Learning Operations)

MLOps extends DevOps principles to the machine learning lifecycle.

MLOps Phases

  • Data collection & versioning
  • Model training & validation
  • Model packaging & deployment
  • Model monitoring & retraining

Core Tools

  • MLflow – experiment tracking
  • DVC – data version control
  • Kubeflow – ML pipelines on Kubernetes
  • TensorFlow Extended (TFX)
  • Airflow / Prefect – orchestration
  • Weights & Biases (W&B) – experiment logging

Deployment Options

  • Model serving: TensorFlow Serving, TorchServe, BentoML
  • APIs: FastAPI / Flask
  • Monitoring: Prometheus + Grafana

MLOps Best Practices

  • Automate retraining
  • Track data & model drift
  • Store metadata and experiment logs
  • Secure model endpoints

12. CI/CD in DevOps, DevSecOps & MLOps

Pipeline Type Focus Key Tools
DevOps CI/CD Code build, test, deploy Jenkins, GitHub Actions, ArgoCD
DevSecOps CI/CD Secure build pipelines Snyk, Trivy, Vault, SonarQube
MLOps CI/CD Model versioning & retraining MLflow, Kubeflow, Airflow

13. Automation & Scripting (Advanced)

  • Automate server setup with Ansible
  • Automate Docker image creation
  • Create Jenkins pipelines (Groovy scripts)
  • Write Bash/Python scripts for log parsing, backups, cron jobs
  • Use REST APIs for tool integration
  •  

14. Collaboration & Agile Practices

  • Agile methodology (Scrum / Kanban)
  • Jira / Trello / ClickUp for task tracking
  • Confluence / Notion for documentation
  • Slack / Teams for communication
  • Git-based collaboration (Pull Requests, Reviews, Branch Policies)

15. Common DevOps Toolchain (2025)

Category

Tools

Version Control

Git, GitHub, GitLab

CI/CD

Jenkins, GitLab CI, CircleCI, ArgoCD

Containers

Docker, Podman

Orchestration

Kubernetes, Helm, Kustomize

IaC

Terraform, Ansible

Monitoring

Prometheus, Grafana

Logging

ELK / EFK Stack

Security

Vault, Trivy, Snyk

Cloud

AWS, Azure, GCP

MLOps

MLflow, Kubeflow, Airflow

16. Real-World Projects to Build Expertise

  • CI/CD Pipeline for Microservices

    • Jenkins + Docker + Kubernetes + Helm

    • Auto build, test, deploy pipeline

  • Infrastructure as Code on AWS

    • Terraform + Ansible + EC2 + S3

  • Monitoring System

    • Prometheus + Grafana dashboard for cluster health

  • Secure Image Deployment

    • Docker + Trivy + Vault + Jenkins

  • MLOps Project

    • MLflow + Airflow + Kubernetes model deployment

  • DevSecOps Pipeline

    • Code scan → build → security test → deploy → monitor

Each project should have:

  • Documentation (README, architecture diagram)
  • GitHub repository
  • Deployment demo (local/cloud)

17. Advanced Concepts (For Expert Level)

  • GitOps (continuous delivery via Git)
  • Chaos Engineering (Gremlin, Litmus)
  • Blue/Green, Canary Rollouts (Argo Rollouts)
  • Serverless DevOps (AWS Lambda, Cloud Functions)
  • Edge Computing deployments
  • Policy as Code (OPA, Kyverno)
  • Cost Optimization & Cloud FinOps

18. Certifications for Career Growth

  • AWS Certified DevOps Engineer – Professional
  • Microsoft Certified: DevOps Engineer Expert
  • Google Professional DevOps Engineer
  • HashiCorp Certified: Terraform Associate
  • Docker Certified Associate
  • CKA / CKAD (Kubernetes)
  • Certified Jenkins Engineer
  • DevSecOps Foundation Certification

19. Soft Skills & Mindset

  • Systems thinking & troubleshooting
  • Team collaboration & communication
  • Problem-solving under pressure
  • Process documentation
  • Continuous learning & improvement mindset

20. How Much Effort It Takes to Become an Expert DevOps Engineer

Phase Focus Area Duration (Approx.)
Foundations Linux, Networking, Git, Scripting 3–4 months
CI/CD & Automation Jenkins, Pipelines, Docker 4–5 months
IaC & Cloud Terraform, AWS, Kubernetes 5–6 months
Security & Monitoring DevSecOps, Prometheus, ELK 4 months
MLOps & Advanced Tools MLflow, Kubeflow, Airflow 3–4 months

Total Estimated Time: ⏱️ Around 18–24 months of continuous learning and hands-on practice.

Effort Required:

  • Minimum 20–25 hours/week of dedicated learning
  • Regular hands-on labs and deployments on cloud
  • 3–5 full DevOps projects to master tools integration
  • Continuous reading of tool documentation & changelogs

⚠️ Disclaimer

This roadmap outlines the complete journey to become an expert-level DevOps Engineer, covering all critical phases — CI/CD, Containers, IaC, Cloud, Monitoring, Security (DevSecOps), and ML integration (MLOps).
However, DevOps is not static — tools evolve monthly, and cloud-native technologies advance rapidly.
To remain a top-tier engineer, one must keep experimenting, contributing to open-source, reading tool docs, and adapting to change continuously.
DevOps excellence demands discipline, curiosity, and relentless automation.