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