FlFe

FlFe is an open-source software framework and data format designed to support flexible feature extraction in machine learning and data analysis. It provides a declarative language and modular components to define, assemble, and reuse features across datasets and models, enabling reproducible experiments and easier feature management.

The project began in the early 2020s as a collaboration among data scientists and software engineers seeking to standardize feature engineering pipelines. It aims to complement traditional feature stores by focusing on the derivation logic and provenance of features as they move through experiments.

FlFe's architecture centers on a core engine and pluggable modules: data adapters for input sources, a library of feature transformers, a declarative pipeline language, and backends for storage and caching. It emphasizes reproducibility through feature lineage tracking, versioned pipelines, and deterministic evaluation.

Features include lazy evaluation, caching of intermediate results, scalable execution, cross-language bindings, and integration hooks with popular ML frameworks. The framework supports both batch and streaming data and can operate in local, cluster, or cloud environments.

Adoption is modest but growing in research settings and mid-size enterprises. It is commonly used to prototype features, run ablation studies, and maintain a centralized feature catalog. Critics note that the ecosystem remains fragmented and that mastering the declarative language can require a learning curve.