Data science and machine learning form the core of Faire’s industry-celebrated marketplace (a16z top-ranked marketplace), driving powerful search, navigation, and risk functions. These functions are powered by ML models trained on over 3000 features defined by their data scientists.
Previously, the process of defining, backfilling and maintaining feature lifecycle was error-prone. To address this challenge, Faire’s data team partnered with Astronomer and chose Apache Airflow on Astro as their tool to democratize their ML feature store framework.
In this webinar, we talked with Faire about how they leverage Airflow and Astro to power their ML training and extend it with a custom framework that powers their feature stores. For a publicly available example highlighting some of the Airflow features described in Faire’s use case, see our example ELT and ML pipeline.
