Project: AI-Driven Product Platform
Category: AI/ML Product
Technologies: Python, TensorFlow, FastAPI, React
Impact: 40% improvement in user engagement through intelligent personalization
Overview
Developed an intelligent product recommendation platform that leverages machine learning to deliver personalized user experiences at scale, serving 10M+ users daily.
Core Components
Recommendation Engine
- Collaborative filtering with matrix factorization
- Content-based filtering using embeddings
- Hybrid approach combining both techniques
- Real-time inference with <100ms latency
Machine Learning Pipeline
- Feature engineering using user and item interactions
- Model training with continuous online learning
- A/B testing framework for model evaluation
- Automated retraining pipeline
API Layer
- FastAPI for high-performance REST API
- GraphQL for flexible querying
- Redis caching for performance
- Rate limiting and security
Results & Impact
- 40% improvement in click-through rate
- 35% increase in conversion rate
- 25% improvement in user retention
- Scaled to 10M+ daily active users
Technical Excellence
- Clean, Pythonic code architecture
- Comprehensive test coverage (>90%)
- Detailed comments and documentation
- Microservices-based design
- Kubernetes orchestration for scaling