Resource library

We’ve developed new approaches to scale human preferences and align LLM output to enterprise users’ expectations by magnifying SME impact.

Enterprises that aim to build valuable GenAI applications must view them from a systems-level. LLMs are just one part of an ecosystem.

Snorkel AI’s Jan. 25 Enterprise LLM Summit focused on one theme: AI data development drives enterprise AI success.

QBE Ventures made a strategic investment in Snorkel AI because it provides what Insurers need: scalable and affordable ways to customize AI.

Snorkel researchers’ state-of-the-art methods created a 7B LLM that ranked 2nd, behind only GPT-4 Turbo, on AlpacaEval 2.0 leaderboard.

Snorkel CEO Alex Ratner spoke with Douwe Keila, an author of the original paper about retrieval augmented generation (RAG).

Labeling training data is a critical and expensive step in producing high accuracy ML models, whether training from scratch or fine-tuning. To make labeling more efficient, two major approaches are programmatic weak supervision (WS) and semi-supervised learning (SSL). More recent works have either explicitly or implicitly used techniques at their intersection, but in various complex and ad hoc ways. In…

Creating large-scale high-quality labeled datasets is a major bottleneck in supervised machine learning workflows. Threshold-based auto-labeling (TBAL), where validation data obtained from humans is used to find a confidence threshold above which the data is machine-labeled, reduces reliance on manual annotation. TBAL is emerging as a widely-used solution in practice. Given the long shelf-life and diverse usage of the resulting…

New unified prompting UI + RAG features, PDF annotation, Databricks MLflow integration, Snorkel Flow Studio, and datasets load 2x faster!