Chalk x Mission Lane Demo: Scaling ML with thousands of interdependent features

Mission Lane’s machine learning platform powers thousands of interdependent features spanning transactional, behavioral, and bureau data. Managing this complexity creates massive feature DAGs where each iteration risks duplication, drift, and costly recomputation. ​In this live demo, Mike Kuhlen (Mission Lane) will share how his team uses Chalk to streamline feature engineering at scale, showing how they: ​Iterate interactively on large DAGs without breaking dependencies ​Reuse and share derived features instead of recomputing ​Whether you’re a data scientist wrangling feature sprawl, a data engineer managing DAG orchestration, or an ML practitioner deploying models in production, you’ll learn practical patterns and real-world lessons for scaling ML systems with thousands of features.