Making User-Sequence Data More Cost-Efficient, Faster, and Easier to Use

This blog post discusses a redesign of the user-sequence platform at Pinterest, aimed at making user sequences for machine learning cheaper, faster, and easier to debug. It details the challenges associated with maintaining high-quality user sequences that power various recommendation systems and shares the architectural improvements made to enhance efficiency, consistency, and operational readiness. Key decisions included adopting a configuration-as-code approach and a shared execution engine, leading to significant reductions in infrastructure costs and faster onboarding of new signal types.