Resource library

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!

The Databricks Model Registry integration equips Snorkel Flow users to automatically register custom, use case-specific models.