Python is widely used in machine learning for its flexibility and expressiveness, but its speed limitations can hinder real-time workflows. To address this, Chalk, a real-time feature platform, developed a Symbolic Python Interpreter to convert Python resolvers into optimized Velox-native expressions, enabling high-performance execution while maintaining Python's user-friendly experience. Chalk's platform allows engineers to define and compute features dynamically, ensuring models have access to the most relevant data. Python resolvers, although flexible, can introduce performance bottlenecks due to Python's global interpreter lock and dynamic typing overhead. Chalk mitigates these issues by executing Python resolvers in subprocesses and through the Symbolic Python Interpreter, which transforms Python functions into efficient Velox expressions. This transformation leverages Velox's multi-threaded and vectorized operations, providing substantial performance improvements. Consequently, Chalk users can write simple Python code for feature computation without sacrificing execution speed, allowing for scalable, real-time ML workloads.