Decoding DevOps – From Basics to Advanced Projects with AI

Decoding DevOps - Complete DevOps Learning Path

This course is designed to take you from DevOps beginner to job-ready DevOps Engineer through hands-on projects, real-world deployments, cloud infrastructure, CI/CD pipelines, Kubernetes, GitOps, Monitoring & Observability, and AI-powered automation.

You'll build and deploy applications across AWS, GCP, Docker, Kubernetes, and GitOps environments while learning the tools, workflows, and best practices used by modern DevOps teams.

Throughout the course, you'll work on real-world projects including multi-tier application deployments, cloud migrations, CI/CD implementations, Kubernetes deployments, Monitoring & Observability setups, and a complete GitOps project using GitHub Actions, Helm, Kubernetes, and ArgoCD.

The course also introduces AI-powered DevOps workflows using GitHub Copilot, Amazon Q, and AI-assisted Helm development to help you automate faster, troubleshoot smarter, and boost productivity.

By the end of this course, you'll have practical experience with Linux, AWS, GCP, Terraform, Ansible, Jenkins, GitHub Actions, GitLab CI/CD, Docker, Kubernetes, Monitoring, GitOps, ArgoCD, and AI-assisted DevOps workflows used in real-world cloud environments.

Foundation Layer

Linux & Infrastructure Fundamentals

• Linux Fundamentals

• Server Management in Linux

• Vagrant

• Networking Fundamentals

• YAML & JSON

• Bash Scripting

• Variables, Conditions & Loops

• Automating Administrative Tasks

Project

• VProfile Project Introduction

• Multi-VM Environment Setup

AI-Assisted Automation Layer

• GitHub Copilot for Scripting & Automation

• AI-Assisted Development Workflows

• Amazon Q for Cloud Automation

• AI-Integrated Helm Workflows

Cloud & Infrastructure Layer

AWS Cloud Fundamentals

• Cloud Computing Concepts

• IAM

• EC2

• EBS

• ELB

• SSM

• CloudShell

• AWS CLI

• S3

• CloudWatch

• RDS

• Auto Scaling

• Route53

Project: Lift & Shift Application to AWS

• Application Migration to AWS

• Cloud Architecture Best Practices

Project: Re-Architecting Applications on AWS

• PaaS-Based Architecture

• SaaS-Based Architecture

• Cloud-Native Design Principles

CI/CD & Automation Layer

Source Control & Build Automation

• Git

• GitHub

• Maven

Jenkins

• CI/CD Pipelines

• Master/Agent Architecture

• Nexus Integration

• SonarQube Integration

• Automated Build & Deployment Workflows

GitHub Actions

• Workflow Automation

• Self-Hosted Runners

• Security Scanning

• CI/CD Pipelines

GitLab CI/CD

• Pipelines

• Stages

• Docker Integration

• Automated Deployments

Python Automation Layer

• Python Fundamentals for DevOps

• OS Automation

• AWS Automation with Python

• Amazon Q Assisted Development

Infrastructure as Code Layer

Terraform

• Terraform Fundamentals

• Variables

• Modules

• Remote State & Backends

• Infrastructure as Code Best Practices

Project

• AWS VPC Automation using Terraform

Monitoring & Observability Layer

Modern DevOps is incomplete without observability. Learn how to collect, visualize, analyze, and act on metrics, logs, and operational data.

Monitoring & Observability

• Monitoring Fundamentals

• Observability Fundamentals

• Why Monitoring Matters in Production

• Prometheus Setup & Configuration

• Grafana Setup & Dashboarding

• Loki for Centralized Logging

• Alloy for Metrics & Logs Collection

• PromQL Fundamentals

• Dashboard Design Best Practices

• Alerting & Notification Strategies

• Slack Integrations

• Centralized Logging Workflows

• Production Monitoring Practices

Configuration Management Layer

Ansible

• Ad Hoc Commands

• Modules

• YAML Fundamentals

• Playbooks

• Variables

• Conditions

• Loops

• Templates

• Handlers

• Roles

AWS Automation with Ansible

• Cloud Provisioning

• Configuration Management

• Deployment Automation

Advanced AWS DevOps Layer

• VPC Deep Dive

• AWS Lambda

• Cloud Logging

• Custom Metrics

• Monitoring & Automation

Project: CI/CD on AWS

• Elastic Beanstalk

• RDS

• CodePipeline

• Automated Deployments

• Production CI/CD Workflows

Google Cloud Platform Project

Multi-Tier Application Deployment on GCP

• Cloud Shell

• VPC

• Firewall Rules

• Virtual Machines

• Cloud SQL

• Memorystore

• Cloud DNS

• Managed Instance Groups

• HTTPS Load Balancers

• Certificate Manager

• Production-Grade Cloud Architecture

Containerization & Kubernetes Layer

Docker

• Containers

• Images

• Dockerfiles

• Volumes

• Networks

• Container Best Practices

Kubernetes

• Kubernetes Architecture

• Cluster Setup

• Pods

• Deployments

• Services

• ConfigMaps

• Secrets

• Ingress

• Autoscaling

• Application Deployments

• Production Workloads

Helm & Kubernetes Tooling

• Helm Fundamentals

• Helm Charts

• AI-Assisted Helm Development

• Lens Kubernetes IDE

Project: VProfile Deployment on Kubernetes

• Containerization

• Kubernetes Deployment

• Service Exposure

• Scaling

• Production Deployment Practices

GitOps & Modern Cloud-Native Delivery

End-to-End GitOps Project

Build a modern GitOps deployment platform using industry-standard cloud-native tools.

• GitHub Actions CI Pipeline

• Automated Docker Image Builds

• Container Registry Integration

• Helm-Based Deployments

• Kubernetes Application Delivery

• ArgoCD Installation & Configuration

• GitOps Workflow Implementation

• Git as the Single Source of Truth

• Automated Application Updates

• Continuous Deployment with ArgoCD

• Production-Style Release Management

• Modern Cloud-Native Delivery Practices

What You'll Achieve

By the end of this course, you will be able to:

• Build and manage cloud infrastructure on AWS and GCP

• Implement Infrastructure as Code using Terraform

• Automate systems using Bash, Python, and Ansible

• Build CI/CD pipelines using Jenkins, GitHub Actions, and GitLab

• Containerize applications with Docker

• Deploy and manage workloads on Kubernetes

• Implement Monitoring & Observability using Prometheus, Grafana, Loki, and Alloy

• Build modern GitOps workflows using ArgoCD and Helm

• Apply AI-powered tools to DevOps automation and development

• Design and manage production-ready cloud-native environments

• Gain the practical skills required for real-world DevOps and Cloud Engineering roles