Braden Hancock

We created Data-centric Foundation Model Development to bridge the gaps between foundation models and enterprise AI. New Snorkel Flow capabilities (Foundation Model Fine-tuning, Warm Start, and Prompt Builder) give data science and machine learning teams the tools they need to effectively put foundation models (FMs) to use for performance-critical enterprise use cases. The need is clear: despite undeniable excitement about…

We are honored to be part of the International Conference on Learning Representations (ICLR) 2022, where Snorkel AI founders and researchers will be presenting five papers on data-centric AI topics The field of artificial intelligence moves fast!  This is a world we are intimately familiar with at Snorkel AI, having spun out of academia in 2019. For over half a…

Moving from Manual to Programmatic Labeling Labeling training data by hand is exhausting. It’s tedious, slow, and expensive—the de facto bottleneck most AI/ML teams face today 1. Eager to alleviate this pain point of AI development, machine learning practitioners have long sought ways to automate this labor-intensive labeling process (i.e., “automated data labeling”) 2, and have reached for classic approaches…

The how, what, and why of Snorkel’s programmatic data labeling approach and the state-of-the-art Snorkel Flow platform. The year was 2015. For the first time, machine learning (ML) had outperformed humans in the annual ImageNet challenge.

Impractical ML assumptions are made every day in research, which limit its adoption. In the real world, these assumptions do not hold up. Learn more about how to avoid making these assumptions about AI application development.