Time to deploy new models to production decreased to just 1 month from 2 – 4 months.
Engineering time recovered from continuing to build internal feature store.
Resources repurposed from maintaining internal feature store.
Tide is a UK-based mobile-first banking platform for small and medium-sized enterprises, offering business bank accounts and services to over 300,000 members. Tide thrives on making data-driven decisions to help their customers save time and money. After facing challenges building production ML data pipelines and an internal feature store, Tide’s dedicated team of data scientists and engineers implemented the Tecton enterprise feature store to build and deploy ML features. As a core component of Tide’s ML stack, Tecton enables a number of ML use cases including fraud detection, risk mitigation, and real-time invoice matching for transactions. With Tecton, Tide accelerated the time to build models, tripled the number of features used per model, and saved the significant engineering time and headcount required to build and maintain an internal feature store.