Real-time ML: Accelerating Python for inference (below 10ms) at scale - Feature Store Summit 2025

Real-time ML: Accelerating Python for inference (below 10ms) at scale Real-time machine learning depends on features and data that by definition can’t be pre-computed. Detecting fraud or acute diseases like sepsis requires processing events that emerged seconds ago. How do we build an infrastructure platform that executes complex data pipelines (below 10ms) end-to-end and on-demand? All while meeting data teams where they are–in Python–the language of ML. We’ll share how we built a symbolic python interpreter that accelerates ML pipelines by transpiling Python into DAGs of static expressions. These expressions are optimized and run at scale with Velox–an OSS (~4k stars) unified query engine (C++) from Meta. About the Speaker: Chase Haddleton is an engineer at Chalk, where he works on the company’s high-performance query engine. Before joining Chalk, he was part of the equities data platforms team at Citadel, building large-scale systems for market data. He holds a Computer Science degree from the University of Waterloo.