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How do we build an infrastructure platform that executes complex data pipelines (< 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.
Chase Haddleton is a Software Engineer at Chalk, where he focuses on building high-performance query engines for real-time data platforms. Before joining Chalk, he contributed to large-scale systems on the equities data platforms team at Citadel. He holds a degree in Computer Science from the University of Waterloo.
