Horizontally Scalable ML Pipelines with a Feature Store

Alexandru A. Ormenisan, Mahmoud Ismail, Kim Hammar, Robin Andersson, Ermias Gebremeskel, Theofilos Kakantousis, Antonios Kouzoupis, Fabio Buso, Gautier Berthou, Jim Dowling, Seif Haridi.

Machine Learning (ML) pipelines are the fundamental building block for productionizing ML models. However, much introductory material for machine learning and deep learning emphasizes ad-hoc feature engineering and training pipelines to experiment with ML models. Such pipelines have a tendency to become complex over time and do not allow features to be easily re-used across different pipelines. Duplicating features can even lead to correctness problems when features have different implementations for training and serving. In this demo, we introduce the Feature Store as a new data layer in horizontally scalable machine learning pipelines.