End-to-End Machine Learning Pipeline
Published:
Project Overview
Designed and implemented a complete machine learning pipeline that processes data, trains models, and serves predictions in real-time. The system handles data ingestion, preprocessing, model training, validation, and deployment with full monitoring and observability.
Key Features
- Real-time Data Processing: Apache Kafka for streaming data ingestion
- Model Training: Automated training pipeline with hyperparameter optimization
- Model Serving: RESTful API with automatic scaling
- Monitoring: Comprehensive logging and metrics collection
- A/B Testing: Framework for comparing model versions
Technical Stack
- Backend: Python, FastAPI, Celery
- Data Processing: Apache Spark, Pandas
- ML Framework: TensorFlow, Scikit-learn
- Infrastructure: Docker, Kubernetes, AWS
- Monitoring: Prometheus, Grafana, ELK Stack
Results
- Reduced prediction latency by 60%
- Improved model accuracy by 15%
- Achieved 99.9% uptime
- Processed 1M+ predictions per day
Lessons Learned
This project taught me the importance of:
- Designing for scale from the beginning
- Implementing comprehensive testing strategies
- Building observability into every component
- Planning for failure and recovery scenarios