Distillation techniques allow enterprises to access the full predictive power of large language models at a tiny fraction of their cost.

Gideon Mann, head of ML Product and Research at Bloomberg LP, chatted with Snorkel CEO Alex Ratner about building BloombergGPT.

Professionals in the data science space often debate whether RAG or fine-tuning yields the better result. The answer is “both.”

Past U.S. Chief Data Scientist DJ Patil talked with Snorkel AI CEO Alex Ratner on topics including the origin of the title “data scientist.”

The surest way to improve foundation models is through more and better data, but Snorkel researchers showed FMs can learn from themselves.

Generative AI can write poems, recite common knowledge, and extract information. GenAI can also help quickly build predictive pipelines.

Getting better performance from foundation models (with less data)

GenAI may be the most transformative technology of the past decade but data is where enterprises are able to realize real value from AI today.

We used weak supervision to programmatically curate instruction tuning data for open-source LLMs to build a better GenAI.

Snorkel and affiliated academic labs have been hard at work reducing how computationally expensive large language models are.

Enterprises—especially the world’s largest—are excited to use large language models, but they want to fine-tune them on proprietary data.

Peter Mattson, Google senior staff engineer and president of MLCommons.org, explained MLCommons at The Future of Data-Centric AI in 2022.

Large language models have enormous potential. But what are they? Where did they come from? And how can you make them work better?

Stanford assistant professor James Zou, presents “Responsible Data-Centric AI for Healthcare and Medicine” at The Future of Data-Centric AI.

Snorkel AI has accepted the first batch of applications for its first annual virtual poster competition. But there’s still time to add yours to the mix.