Handling Model Drift in Production ML Systems
How to detect and handle model drift in production ML systems, with practical techniques, code examples, and lessons from scaling to 5000+ stocks.
Machine Learning
Writing
Technical notes, tutorials, and thoughts on building production AI systems.
Scaling from 48 to 5,000 Stocks: What Broke and How I Fixed It
When I built the MVP for Algorion AI, we predicted 48 stocks. Scaling to 5,000 broke everything. Here's what I learned about building production ML systems that actually scale.
System Design
Why I Build Systems, Not Just Models
Why production machine learning requires systems thinking beyond Jupyter notebooks and standalone models.
Machine Learning
Building Scalable APIs with FastAPI
Designing high-performance, scalable APIs with FastAPI using async I/O, connection pooling, and caching strategies.
FastAPI
Docker Containers for ML Deployment
Deploying machine learning models reliably using Docker containers, multi-stage builds, and production-ready practices.
Docker