2.3k citations · 2.4k across the 11 of their papers we have counts for
3 papers · 1 filter
Can Foundation Models Wrangle Your Data?
Avanika Narayan, Ines Chami, Laurel Orr +2
Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning. As these models con…
Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems
Laurel Orr, Atindriyo Sanyal, Xiao Ling +2
The industrial machine learning pipeline requires iterating on model features, training and deploying models, and monitoring deployed models at scale. Feature stores were developed…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…