Chalk is data infrastructure for machine learning with a best-in-class developer experience. It enables data teams to declare features and their dependencies with idiomatic Python in online, streaming, and batch environments. Chalk compiles these definitions into parallel pipelines that run on a Rust-based engine. Significantly, Chalk uses the exact same source code to serve temporally-consistent training sets to data scientists and live feature values to models. This re-use ensures that feature values from online and offline contexts match and dramatically cuts development time.
Taken together, innovative machine learning teams can focus on their unique products while Chalk seamlessly handles data infrastructure.