Python is the language of choice for many ML teams — it’s flexible, expressive, and has the right ergonomics for real-time workflows. But it’s also slow.

To make Python faster, I built a Symbolic Python Interpreter to make Chalk’s “Python resolvers” into optimized Velox-native expressions — enabling high-performance execution without sacrificing developer experience.

This post explains the problem I encountered, why it’s so challenging, and how we solved for it.