AI Interview Pro - End-to-End DevOps Deployment Project

An end-to-end DevOps project demonstrating Infrastructure as Code (Terraform), Configuration Management (Ansible), CI/CD (GitHub Actions), Containerization (Docker), Container Registry (Docker Hub), Kubernetes Orchestration (K3s), Ingress Routing (Traefik), and Cloud Deployment on AWS EC2.

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Project Overview

The goal of this project was to deploy a full-stack MERN application using modern DevOps practices.

Instead of deploying the application manually, the complete infrastructure and deployment process was automated using DevOps tools.

This project demonstrates real-world DevOps workflows including:

• Infrastructure Provisioning
• Configuration Management
• Containerization
• CI/CD Automation
• Kubernetes Deployment
• Ingress Routing
• Cloud Hosting

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🏗 Architecture Diagram

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Complete Workflow

Developer
   ↓
GitHub Repository
   ↓
GitHub Actions CI/CD
   ↓
Docker Hub
   ↓
Terraform
   ↓
AWS EC2
   ↓
Ansible
   ↓
K3s Kubernetes Cluster
   ↓
Traefik Ingress
   ↓
Frontend Pod
   ↓
Backend Pod
   ↓
MongoDB Atlas

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⚙️ Tech Stack

Cloud

• AWS EC2

Infrastructure as Code

• Terraform

Configuration Management

• Ansible

Containerization

• Docker
• Docker Hub

CI/CD

• GitHub Actions

Container Orchestration

• K3s Kubernetes

Networking

• Traefik Ingress Controller

Application

Frontend

• React.js
• Vite
• Tailwind CSS

Backend

• Node.js
• Express.js

Database

• MongoDB Atlas

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Problem Statement

Deploying applications manually leads to:

• Configuration drift
• Manual errors
• Difficult scaling
• Slow deployments
• Inconsistent environments

To solve this problem, the entire deployment pipeline was automated.

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Step 1: Infrastructure Provisioning with Terraform

Terraform was used to provision AWS infrastructure.

Resources Created:

• EC2 Instance
• Security Group
• SSH Key Pair
• Networking Configuration

Terraform Commands

terraform init
terraform plan
terraform apply

Terraform Init

Terraform Plan

Terraform Apply

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Step 2: AWS EC2 Deployment

The infrastructure was created successfully on AWS.

AWS EC2 Running

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Step 3: Containerization with Docker

Frontend and Backend applications were containerized using Docker.

Docker images were built and pushed to Docker Hub.

Images:

• ayusnath/ai-frontend
• ayusnath/ai-backend

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Step 4: Docker Hub Registry

Docker Hub was used as a centralized image registry.

Docker Hub Images

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Step 5: CI/CD using GitHub Actions

A GitHub Actions workflow was created.

Workflow Responsibilities:

• Build Docker Images
• Push Images to Docker Hub
• Automate Deployment Pipeline

Pipeline Trigger:

on:
  push:
    branches:
      - main

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Step 6: Server Configuration using Ansible

Ansible was used to automate server configuration.

Tasks Automated:

• Docker Verification
• Container Management
• Deployment Automation

Ansible Success

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Step 7: Kubernetes Cluster Setup (K3s)

K3s was installed on AWS EC2.

Why K3s?

• Lightweight
• Low Memory Usage
• Easy Setup
• Perfect for Cloud Learning Projects

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Kubernetes Node Verification

Kubernetes Node

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Kubernetes Pods

Frontend and Backend applications were deployed as Pods.

Kubernetes Pods

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Kubernetes Services

Services were created for internal communication and external exposure.

Kubernetes Services

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Traefik Ingress Controller

Traefik was used as the Kubernetes Ingress Controller.

Responsibilities:

• Request Routing
• Load Balancing
• External Access

Kubernetes Ingress

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MongoDB Atlas Integration

MongoDB Atlas was used as a managed cloud database.

Benefits:

• No database management overhead
• High availability
• Secure cloud storage

Application Flow:

Frontend
   ↓
Backend API
   ↓
MongoDB Atlas

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Live Application

The application was successfully deployed and accessed through Kubernetes Ingress.

Live Application

Github Action

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Project Achievements

Successfully implemented:

• Terraform Infrastructure Provisioning

• AWS EC2 Deployment

• Docker Containerization

• Docker Hub Integration

• GitHub Actions CI/CD

• Ansible Automation

• K3s Kubernetes Cluster

• Traefik Ingress Controller

• MongoDB Atlas Integration

• Full Stack Deployment

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Key Learnings

During this project I learned:

• Infrastructure as Code
• AWS Cloud Deployment
• Docker Containerization
• CI/CD Pipelines
• Kubernetes Concepts
• Ingress Controllers
• Configuration Management
• Production Deployment Workflow

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Future Improvements

• Custom Domain Integration
• HTTPS using Let's Encrypt
• Monitoring with Prometheus
• Grafana Dashboards
• Kubernetes HPA
• Multi-Node Kubernetes Cluster
• ArgoCD GitOps Deployment

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Author

Ayush Nath

GitHub: https://github.com/developergith

Linkedin: https://www.linkedin.com/in/ayush-nath-motichoor-7012102b2/

Medium.com https://medium.com/@ayushmotichoor

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