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The industrial machine learning pipeline requires iterating on modelfeatures, training and deploying models, and monitoring deployed models atscale. Feature stores were developed to manage and standardize the engineer'sworkflow in this end-to-end pipeline, focusing on traditional tabular featuredata. In recent years, however, model development has shifted towards usingself-supervised pretrained embeddings as model features. Managing theseembeddings and the downstream systems that use them introduces new challengeswith respect to managing embedding training data, measuring embedding quality,and monitoring downstream models that use embeddings. These challenges arelargely unaddressed in standard feature stores. Our goal in this tutorial is tointroduce the feature store system and discuss the challenges and currentsolutions to managing these new embedding-centric pipelines.

Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems