🎯 DevOps Final Project - Face Detection App Deployment

A complete DevOps pipeline for deploying a Next.js face detection application using modern DevOps practices and tools.

🚀 Application Overview

This project includes a custom-built Next.js application (next-face-detection-app) as a submodule, which:

• 📸 Implements real-time face detection using webcam stream
• ⚡ Built with Next.js and TypeScript
• 🧠 Uses face-api models for detecting face landmarks, age, gender and emotion
• 🐳 Containerized using Docker
• 🔄 Automatically updated through CI/CD pipeline

✨ Application Features

• 🎥 Live video stream processing
• 👤 Real-time face detection
• 📱 Responsive web interface
• 🚀 Optimized Docker container

Docker Implementation

• Multi-stage build process
• Optimized image size
• Configured for both development and production
• Automated builds via GitHub Actions

🛠️ Prerequisites

• ☁️ AWS Account with appropriate permissions
• 🐳 Docker installed
• ⚓ kubectl installed
• 🏗️ Terraform installed
• 🔧 AWS CLI configured

🔄 Project Flow

1. 🔄 CI/CD Pipeline

graph LR
    A[Code Push] --> B[Update Submodule]
    B --> C[Quality Checks]
    C --> D[Build Docker Image]
    D --> E[Push to Registry]
    E --> F[Webhook Trigger]
    F --> G[Update K8s]
    G --> H[Rolling Update]
    H --> I[Health Check]

    classDef default fill:#ffffff,stroke:#e2e8f0,color:#4a5568,font-family:arial
    classDef ci fill:#5a67d8,stroke:#4c51bf,color:#fff,font-family:arial
    classDef cd fill:#48bb78,stroke:#38a169,color:#fff,font-family:arial

    class A,B,C,D,E ci
    class F,G,H,I cd

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💡 CI/CD Details

When developers push changes, it triggers an automated pipeline that runs quality checks, builds a new Docker image, and updates the Kubernetes deployment with zero downtime.

2. 🏗️ Infrastructure Deployment

graph TD
    A([terraform init]):::init --> B([terraform plan]):::plan
    B --> C([terraform apply]):::apply
    C --> D([AWS Resources]):::aws
    D --> E([K8s Cluster]):::k8s

    classDef init fill:#4299e1,stroke:#3182ce,color:#fff,font-family:arial,font-weight:bold
    classDef plan fill:#48bb78,stroke:#38a169,color:#fff,font-family:arial,font-weight:bold
    classDef apply fill:#5a67d8,stroke:#4c51bf,color:#fff,font-family:arial,font-weight:bold
    classDef aws fill:#f6ad55,stroke:#ed8936,color:#fff,font-family:arial,font-weight:bold
    classDef k8s fill:#667eea,stroke:#5a67d8,color:#fff,font-family:arial,font-weight:bold

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💡 Infrastructure Details

The infrastructure is provisioned using Terraform, which creates all necessary AWS resources including VPC, subnets, EC2 instances for Kubernetes nodes, and security groups. Once complete, a fully functional Kubernetes cluster is ready for deployments.

3. ⚓ Kubernetes Implementation

graph TD
    N([Namespace]):::namespace --> A([Ingress]):::ingress
    N --> B([Service]):::svc
    N --> C([Deployment]):::deploy
    N --> G([ConfigMap]):::config

    A --> B
    B --> C
    C --> D([Pod 1]):::pod
    C --> E([Pod 2]):::pod
    C --> F([Pod 3]):::pod

    G -.configures.-> D
    G -.configures.-> E
    G -.configures.-> F

    classDef namespace fill:#e53e3e,stroke:#c53030,color:#fff,font-family:arial,font-weight:bold
    classDef ingress fill:#f6ad55,stroke:#ed8936,color:#fff,font-family:arial,font-weight:bold
    classDef svc fill:#4299e1,stroke:#3182ce,color:#fff,font-family:arial,font-weight:bold
    classDef deploy fill:#48bb78,stroke:#38a169,color:#fff,font-family:arial,font-weight:bold
    classDef pod fill:#667eea,stroke:#5a67d8,color:#fff,font-family:arial,font-weight:bold
    classDef config fill:#9f7aea,stroke:#805ad5,color:#fff,font-family:arial,font-weight:bold

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💡 Kubernetes Details

The application runs in a Kubernetes cluster with multiple pods for high availability. Configuration is managed through ConfigMaps and Secrets, while traffic is routed through Services and Ingress.

4. 🏛️ Infrastructure Overview

graph TD
    A([AWS Infrastructure]):::aws --> B([VPC]):::vpc
    B --> SG1([Security Groups]):::sg
    B --> C([K8s Cluster]):::k8s

    SG1 -.secures.-> C
    C --> D([Production]):::prod
    C --> E([Staging]):::stage

    D --> DP([3 Pod Replicas]):::pod
    E --> SP([2 Pod Replicas]):::pod

    SG2([Load Balancer SG]):::sg -.secures.-> D
    SG3([K8s SG]):::sg -.secures.-> D
    SG2 -.secures.-> E
    SG3 -.secures.-> E

    classDef aws fill:#f6ad55,stroke:#ed8936,color:#fff,font-family:arial,font-weight:bold
    classDef vpc fill:#4299e1,stroke:#3182ce,color:#fff,font-family:arial,font-weight:bold
    classDef sg fill:#fc8181,stroke:#f56565,color:#fff,font-family:arial,font-weight:bold
    classDef k8s fill:#667eea,stroke:#5a67d8,color:#fff,font-family:arial,font-weight:bold
    classDef prod fill:#48bb78,stroke:#38a169,color:#fff,font-family:arial,font-weight:bold
    classDef stage fill:#9f7aea,stroke:#805ad5,color:#fff,font-family:arial,font-weight:bold
    classDef pod fill:#5a67d8,stroke:#4c51bf,color:#fff,font-family:arial,font-weight:bold

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💡 Infrastructure Overview Details

The project runs on AWS with separate environments for production and staging, each with its own Kubernetes cluster. Production runs with higher availability using 3 pod replicas, while staging uses 2 replicas for cost efficiency.

🏗️ Technical Architecture

🔄 CI/CD Pipeline with GitHub Actions

1. 📦 Submodule Update Workflow

   • Automatically detects changes in the next-face-detection-app
   • Updates the submodule in this repository
   • Triggers the quality checks pipeline

2. ✅ Quality Checks & Docker Build

   • Runs after submodule updates
   • Performs TypeScript, ESLint, and formatting checks
   • Builds and pushes Docker image to Docker Hub

3. 🚀 Continuous Deployment

   • Triggered by Docker Hub webhooks
   • Connects to Kubernetes cluster using GitHub Secrets
   • Updates the application deployment with zero downtime

⚓ Kubernetes Resources

1. 🔍 Namespace

   • Named 'face-detection'
   • Provides logical separation of workloads

2. 🚀 Deployment

   • Manages application pods
   • Handles rolling updates
   • Controls replica count and resource allocation

3. 🔌 Service

   • Exposes the application within the cluster
   • Manages internal load balancing
   • Routes traffic to application pods

4. 🌐 Ingress

   • Handles external access to the service
   • Manages SSL/TLS termination
   • Configures routing rules

🔐 Security

• 🛡️ AWS security groups for network isolation
• 🔑 SSH key authentication for server access
• 🔒 GitHub Secrets for sensitive data
• 🔐 HTTPS enforcement for web traffic

🌍 Environments

1. 🏭 Production

   • High availability setup
   • Multiple worker nodes
   • Production-grade resources

2. 🧪 Staging

   • Testing environment
   • Reduced resource allocation
   • Development validation

📚 Additional Information

• 📝 Installation Guide
• 👥 Contributors
• ✅ Tasks