Introducing Alfred: an open-source tool for combining foundation models with weak supervision for faster development of academic data sets.

This release features new GenAI tools and Multi-Schema Annotation, as well as new enterprise security tools and an updated home page.

Enterprises must evaluate LLM performance for production deployment. Custom, automated eval + data slices present the best path to production.

Meta’s Llama 3.1 405B, rivals GPT-4o in benchmarks, offering powerful AI capabilities. Despite high costs, it can enhance LLM adoption through fine-tuning, distillation, and as an AI judge.

Meta released Llama 3 405B today, signaling a new era of open source AI. The model is ready to use on Snorkel Flow.

High-performing AI systems require more than a well-designed model. They also require properly constructed training and testing data.

We need more labeled data than ever, so we have explored weak supervision for non-categorical applications—with notable results.

Vision language models demonstrate impressive image classification capabilities, but LLMs can help improve their performance. Learn how.

Fine-tuning specialized LLMs demands a lot of time and cost We developed Bonito to make this process faster, cheaper, and easier.

The manufacturing industry has experienced a massive influx of data. Snorkel AI and AWS Sage Maker can make that data actionable.

Snorkel’s Paroma Varma and Google’s Ali Arsenjani discus the role of data in the development and implementation of LLMs.

Snorkel AI placed a model at the top of the AlpacaEval leaderboard. Here’s how we built it, and how it changed AlpacaEval’s metrics.

As Snorkel AI prepares to build better enterprise LLM evaluations, we spoke with Yifan Mail from Stanford’s CRFM HELM project.

Google and Snorkel AI customized PaLM 2 using domain expertise and data development to improve performance by 38 F1 points in a matter of hours.

Humans learn tasks better when taught in a logical order. So do LLMs. Researchers developed a way to exploit this tendency called “Skill-it!”