Share via

AI feature management

  • Article

What is a Feature?

Features are descriptive attributes about a dataset that help in model prediction, forecasting, and more.

Raw data is rarely in a format that can be consumed by an ML model directly, so it needs to be transformed into features. This process is called feature engineering.

What is a Feature Store?

A Feature Store is a software toolkit designed to manage raw data transformations into features. Feature Stores often include metadata management tools to register, share, and track features. Feature stores also handle the complexity of doing correct point-in-time joins. So, the resulting data frame can be ingested by any model training libraries.

The Feature Store Adoption Guide dives deeper into whether a Feature Store is right for your project. The Feature Store Comparison Guide makes comparisons between the popular feature stores to help you pick the best fit for your use case.

How Feature Stores simplify the ML Model Workflow?

A typical ML Model workflow (for example, without Feature Stores) generally means one has to build multiple models using hundreds of features. The maintenance of the feature transformation pipelines alone becomes tedious, slowing down the productivity and efficiency of model development. Also, the design isn't easily reuseable and doesn't promote sharing of features across teams.

MLFlow without feature store

The following drawing illustrates workflow change with the introduction of Feature Stores.

MLFlow with feature store