ShopMLOps: End-to-End E-commerce with ML Recommendation System

ShopMLOps is a high-performance e-commerce platform integrated with Machine Learning Operations (MLOps). It features real-time product recommendations, advanced search, and a modern microservices-ready architecture.

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🚀 How It Works

1. Data Generation: A synthetic engine generates thousands of realistic products, categories, and user events (views, cart adds, transactions).
2. Infrastructure: Docker launches a suite of services:
   • PostgreSQL: Handles core transactions (Users, Orders).
   • MongoDB: Stores event logs for ML training.
   • Elasticsearch: Provides lightning-fast full-text search.
   • Redis: Caches recommendations and sessions.
3. ML Engine: A Python service computes TF-IDF Vectorization and Cosine Similarity to suggest products based on item attributes and user behavior.
4. Frontend: A Next.js 14 application provides a seamless, responsive UI with real-time updates.

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🛠️ Prerequisites

Ensure you have the following installed on your system:

• Docker & Docker Compose (Required for database services)
• Node.js (v18 or higher)
• Python (3.10 or higher)
• Git

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🚦 Installation & Setup

1. Clone the repository

git clone https://github.com/Tanuj-a-bit/mlops.git
cd mlops

2. Database & Infrastructure (Docker)

This is universal for Windows (WSL2/Desktop), macOS, and Linux.

cd Backend
docker-compose up -d

Wait for all containers (Postgres, Mongo, Redis, Elasticsearch) to show "Started".

3. Backend Setup (Python)

🪟 Windows

python -m venv venv
.\venv\Scripts\activate
pip install -r requirements.txt

🍎 macOS / 🐧 Linux

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

4. Data Generation & Seeding

Populate the system with products and events:

# Run from the root directory
python generate_data.py

# Seed the database
cd Backend
npx prisma generate
DATABASE_URL="your_postgresql_url" npx tsx prisma/seed.ts

5. Frontend Setup (Next.js)

cd ../Frontend
npm install
npx prisma generate
npx prisma db push
npm run dev

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💻 Cross-Platform Guide

Feature	Windows (PowerShell)	macOS / Linux (Terminal)
Python Command	python	python3
Virtual Env	.\venv\Scripts\activate	source venv/bin/activate
Env Variables	$env:DATABASE_URL="url"	export DATABASE_URL="url"
File Paths	Uses backslashes \	Uses forward slashes /

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🤖 Testing the ML Engine

You can manually trigger the recommendation engine from the CLI:

# Content-based (Item-to-Item)
python ml/recommend.py item 1001

# Collaborative (User-to-Item)
python ml/recommend.py user 5

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📜 Project Structure

├── Backend/          # FastAPI, Prisma, Docker, Database Schemas
├── Frontend/         # Next.js 14, Tailwind CSS, UI Components
├── Dataset/          # Generated CSV data
├── ml/               # Recommendation system logic
└── generate_data.py  # Synthetic data engine

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📝 License

MIT License. Created by Tanuj.