text: 333,141,1427678621058027529,1193288437052260352,Laurel Orr,"Ever woke up thinking “what will ML pipelines look like in the next few years?”. Come to our tutorial “ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems” 8/18/21 7:15am PT at VLDB to find out what we think the future will hold. <LINK> Our take: self-supervised ecosystems, where embedding representations are learned over massive corpora and integrated into hundreds of downstream systems, are shifting the ML pipeline from the manual feature curation and data labeling of Feature Stores to hands-free training. In this new self-supervised paradigm, engineers face challenges with respect to overcoming potential biases (e.g., popularity bias) in the uncurated training data, managing embedding stability, and continually monitoring and maintaining models. We will discuss these challenges and exciting open problems tomorrow! This tutorial would not have been possible without my amazing collaborators @atinsanyal @lingxiao @m_leszczy @krandiash",https://arxiv.org/abs/2108.05053,"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. ","Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding