Stripe adapts Chronon to scale ML feature development
Stripe has open sourced Shepherd, an internal tool that adapts Chronon to scale machine learning feature development across its infrastructure. The company detailed the work in a new engineering post.
Shepherd addresses the challenge of managing feature pipelines at Stripe's scale, where the company operates ML systems supporting products like Radar, its fraud prevention solution. Stripe engineers built Shepherd to make Chronon—an open source feature platform—more accessible and efficient for teams building and deploying ML features.
The tool is designed to streamline the workflow of defining, testing, and shipping features for production ML models. By adapting Chronon's capabilities, Shepherd aims to reduce the operational overhead that typically comes with running feature engineering pipelines at large scale.
Stripe's ML infrastructure team, which includes Ben Mears, the post's author, has been iterating on the approach as the company's ML footprint has grown alongside products such as Radar. The engineering post highlights how Shepherd fits into Stripe's broader push to make its ML feature store execution more developer-friendly.
