AI-Powered E-commerce Visual Search & Recommendation System

Author: Regis Nde Tene (Chopinregis)

Status: Draft

Duration: 1 Week Deep Dive

Executive Summary

Designed and developed an end-to-end e-commerce application featuring a computer vision-driven visual search and a personalized product recommendation engine. This project integrated frontend UI, backend machine learning model deployment on the cloud, and data science principles to optimize user experience and conversion.

Key Skills

Project Execution Log

Stage 1: UX/UI Design & Frontend Prototyping

This stage successfully established the user interface and user experience foundation for the AI-powered e-commerce platform. We progressed from conceptual ideas to tangible designs through wireframing and high-fidelity mockups, culminating in a basic design system. A functional frontend prototype was built using HTML, CSS, and JavaScript, demonstrating core functionalities like product display and an interactive visual search input. This prototype provides a robust visual and interactive groundwork, ready for the integration of the backend machine learning models.

Deliverables

Stage 2: Data Acquisition & ML Model Training

This stage successfully laid the groundwork for the AI-powered e-commerce system by acquiring and preparing the necessary data. We developed and trained core machine learning models for visual search and personalized recommendations, transforming raw data into intelligent capabilities.

Deliverables

Stage 3: API Development & Cloud Deployment

This stage focused on transforming the local ML models into a production-ready service. We designed and implemented a RESTful API using Python, containerized it with Docker, and deployed it onto AWS ECS Fargate. This established a scalable and accessible backend interface for the e-commerce system, enabling the frontend to leverage the powerful visual search and recommendation capabilities.

Deliverables

Stage 4: Frontend Integration & CRO Optimization

This stage successfully bridged the gap between the developed machine learning backend and the user-facing interface. We integrated visual search and recommendation APIs into a responsive and intuitive frontend, applying core UX/UI principles. Furthermore, we established critical CRO infrastructure, including analytics tracking and A/B testing capabilities, laying the groundwork for data-driven optimization. This stage solidified my skills in full-stack integration, frontend development, and conversion rate optimization.