apply() Highlight: How to Use Fast & Fresh Features for Online Predictions

Online inference can significantly increase the impact of a machine learning model by incorporating fresh data, but it requires careful engineering to ensure a snappy user experience. To keep latency low during online inference, smaller or faster models and unifying feature pipelines are key considerations. Tecton's Feature Store makes it easy for teams to serve fresh feature data for their online models, allowing Data Scientists to write a single feature definition that can be used in both offline model training and online model inference. The Feature Store simplifies the process of joining together batch, streaming, and request-time data by providing separate features based on their data source through Feature Views, which then define the set of features for the model in a Feature Service.

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