Project Overview
Developed a comprehensive machine learning platform that streamlines the ML lifecycle from data preparation to model deployment and monitoring. The platform enables data scientists to focus on model development while automating infrastructure management.
Key Features
- Automated ML model training pipelines
- Model versioning and experiment tracking
- One-click model deployment to production
- Real-time model performance monitoring
- A/B testing framework for model evaluation
Technical Implementation
- Built using Python and TensorFlow for model training
- Implemented Kubernetes for scalable model serving
- Created custom model registry and versioning system
- Developed automated CI/CD pipelines for ML models
- Integrated monitoring using Prometheus and custom metrics
Impact
- Reduced model deployment time from weeks to hours
- Improved model performance through automated optimization
- Enabled reproducible ML experiments
- Supported 100+ production ML models