Erschienen:
Association for Computing Machinery (ACM), 2021
Erschienen in:
Proceedings of the VLDB Endowment, 14 (2021) 12, Seite 3178-3181
Sprache:
Englisch
DOI:
10.14778/3476311.3476402
ISSN:
2150-8097
Entstehung:
Anmerkungen:
Beschreibung:
The industrial machine learning pipeline requires iterating on model features, training and deploying models, and monitoring deployed models at scale. Feature stores were developed to manage and standardize the engineer's workflow in this end-to-end pipeline, focusing on traditional tabular feature data. In recent years, however, model development has shifted towards using self-supervised pretrained embeddings as model features. Managing these embeddings and the downstream systems that use them introduces new challenges with respect to managing embedding training data, measuring embedding quality, and monitoring downstream models that use embeddings. These challenges are largely unaddressed in standard feature stores. Our goal in this tutorial is to introduce the feature store system and discuss the challenges and current solutions to managing these new embedding-centric pipelines.