  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.
