We worked closely with the client to design and implement a comprehensive Enterprise Feature Store (EFS) architecture, encompassing online and offline feature storage, a specialized segment store for grouping customers, and various sources and interfaces for streamlined data management.

To set up the system, we first identified over 4,000 features related to our client’s subscribers and focused on 1,000 of the most important ones for initial development. We looked at data from multiple sources, like their data warehouse and data lake, to gain valuable insights. By using tools for managing metadata and feature catalogs, we made it easier to explore and reuse features across different projects. We also chose advanced tools to manage and organize the feature store, grouped features by their business impact, and created over 1,000 new features for their machine learning and marketing needs.

Our next step was to design and structure a data model that connected multiple data systems and ensured smooth collaboration across teams using our feature store framework. By collecting data from various sources and organizing it into separate stores, we categorized over 70 million customers based on their features. This simplified data management and helped the client adopt and effectively use AI technologies.