[Update Course] Decoding DevOps – From Basics to Advanced Projects with AI | Udemy

This course is designed for anyone who wants to start or advance their DevOps career through hands-on, project-based learning.

You’ll begin with Linux, networking, and scripting fundamentals, then progress through key DevOps tools — Git, Jenkins, GitHub Actions, GitLab, Terraform, Ansible, Docker, Kubernetes, and AWS Cloud.

Each step builds on the last with real projects, like setting up the Vprofile application across multiple DevOps stages — from on-premise to AWS to Kubernetes.

The course also introduces AI-powered tools such as GitHub Copilot, Amazon Q, and AI-integrated Helm, helping you automate faster, code smarter, and build intelligent DevOps pipelines.

By the end, you’ll master both the core DevOps practices and modern AI-driven workflows, preparing you for real-world cloud and automation environments.

Step 1

• Basics of Linux
• Server Management in Linux
• Vagrant
• Basics of networking
• Project
  • Vprofile Project Intro & Setup on VM’s

Step 3

• YAML & JSON
• Bash Scripting
• Basics of scripting
• Variables, Conditions, Loops etc
• Automating day to day admin tasks
• Github Copilot as AI Code assistants for Scripting

Step 3

• Cloud Computing Intro
• IAM
• Ec2 Instances, EBS Volumes, ELB
• SSM & CloudShell Intro
• AWS CLI
• S3
• Cloudwatch
• RDS
• Autoscaling
• Route53

• Project
  • AWS Cloud For Project Set Up Lift & Shift
  • Re-Architecting Web App on AWS Cloud [PAAS & SAAS]

Step 4

• Version control system = Git & GitHub
• Build Tools | Build & Test Java Code = Maven
• Continuous Integration Intro
• Jenkins
  • Jenkins as a Build Server
  • Jenkins Jobs | Build , Test, Deploy, Notify
  • Jenkins Master/Slave, Nexus, Sonarqube
  • Jenkins CI Pipeline
  • Jenkins Administration
• GitHub Actions
  • What GitHub Actions is and how it fits into DevOps workflows
  • Core concepts: workflows, jobs, steps, and actions
  • Triggers and events (push, pull request, schedule, etc.)
  • Security scans in GitHub Actions
  • Conditions, permissions, and runners for flexible pipelines
  • Hands-on examples to build and run pipelines directly from GitHub
• Gitlab
  • What GitLab CI/CD is and how it fits into modern DevOps workflows
  • Core concepts — pipelines, jobs, stages, runners, variables and artifacts
  • Writing and understanding .gitlab-ci.yml files
  • Setting up build, test, and deploy stages with rules
  • Integrating Docker, security scans, and notifications

     

Step 5

• Python Scripting
• Basics of python programming
• vars, datatypes, conditions, loops, function, modules etc
• Pythons for automating OS tasks
• Python for AWS using Amazon Q code assistant(AI)

Step 6

• Terraform code structure
• Variables
• Plan, Apply, Update & Destroy
• Provisioners
• Outputs
• Backends
• Modules
• Terraform for VPC Setup

Step 7

• • Ansible Intro
  • Ad Hoc commands
  • Modules
  • YAML into
  • Playbooks
    • vars, conditions, loops,
    • handlers, templates etc etc etc
  • Variables deep dive
  • Roles
  • Ansible for AWS

Step 8

• • AWS Part 2
  • VPC in depth
  • Log management and custom metrics
  • AWS Lambda
  • Project
    • Vprofile on Beanstalk & RDS
    • Code Commit, Code Build & Code Pipeline
    • CI & CD on AWS Cloud for Vprofile Project
    • Beanstalk, RDS, CodePipeline etc

Step 9

• • Docker Intro
  • Understanding and Implementing Containers
    • Volumes, Network, logs etc
  • Building Images for Vprofile project
  • Docker compose to run vprofile multi containers
  • Kubernetes Intro
  • Kubernetes setup for production Env
  • Kubernetes objects
    • Pods, Services, Controllers, Deployment
    • Replication, Autoscaling, Resource quotas, secret, configmap, namespace.
    • Ingress
  • Helm with AI
  • Lens
  • Project
    • Vprofile Project deployment on Kubernetes

Step 10

Project on GitOps