How Do Real-Time Features Work in Machine Learning?

Real-time features are transformations of raw data that serve as input signals for ML models in real-time applications. They can be used to detect fraudulent transactions, deliver personalized product recommendations, and compare current context with historical data. Real-time features offer benefits such as lower storage and computation costs, reduced third-party request costs, more stable ML pipelines, and easier integration of new features into pipelines. Tecton's On-Demand Feature Views (ODFVs) allow users to create customized real-time feature pipelines using a declarative feature engineering framework. By computing features on-demand and in real time, ODFVs enable the creation of dynamic models that can adapt to changing data and user behavior.

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