
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
Technical notes, tutorials, and thoughts on building production AI systems.

How to detect and handle model drift in production ML systems, with practical techniques, code examples, and lessons from scaling to 5000+ stocks.

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.

Why production machine learning requires systems thinking beyond Jupyter notebooks and standalone models.

Designing high-performance, scalable APIs with FastAPI using async I/O, connection pooling, and caching strategies.

Deploying machine learning models reliably using Docker containers, multi-stage builds, and production-ready practices.