Learn about the obstacles faced by data scientists in LLM evaluation and discover effective strategies for overcoming them.

Snorkel AI and AWS are partnering to help enterprises build, deploy, and evaluate custom, production-ready AI models. Learn how.

What is AI data development? AI data development includes any action taken to convert raw information into a format useful to AI.

Discover highlights of Snorkel AI’s first annual SnorkelCon user conference. Explore Snorkel’s programmatic AI data development achievements.

We aim to help our customers get GenAI into production. In our 2024.R3 release, we’ve delivered some exciting GenAI evaluation results.

Snorkel AI has made building production-ready, high-value enterprise AI applications faster and easier than ever. The 2024.R3 update to our Snorkel Flow AI data development platform streamlines data-centric workflows, from easier-than-ever generative AI evaluation to multi-schema annotation.

Discover new NLP features in Snorkel Flow\’s 2024.R3 release, including named entity recognition for PDFs + advanced sequence tagging tools.

Discover the latest enterprise readiness features for Snorkel Flow. Configure safeguards for data compliance and security.

Learn how Snorkel, Databricks, and AWS enabled the team to build and deploy small, specialized, and highly accurate models which met their AI production requirements and strategic goals.

“Task Me Anything” empowers data scientists to generate bespoke benchmarks to assess and choose the right multimodal model for their needs.

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

Retrieval-augmented generation (RAG) enables LLMs to produce more accurate responses by finding and injecting relevant context. Learn how.

How one large financial institution used call center AI to inform customer experience management with real-time data.

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.

To tackle generative AI use cases, Snorkel AI + AWS launched an accelerator program to address the biggest blocker: unstructured data.

AI alignment ensures that AI systems align with human values, ethics, and policies. Here’s a primer on how developers can build safer AI.

The Snorkel Flow label model plays an instrumental role in driving the enterprise value we create. Here’s a peek at how it works.

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

Snorkel researchers devised a new way to evaluate long context models and address their “lost-in-the-middle” challenges with mediod voting.

See a walkthrough of how Snorkel Flow users build applications with production-grade RAG retrieval components.

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

Snorkel takes a step on the path to enterprise superalignment with new data development workflows for enterprise alignment

Snorkel Flow’s 2024.R1 release includes new role-based access control tools to further safeguard valuable enterprise data.

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

ROBOSHOT acts like a lens on foundation models and improves their zero-shot performance without additional fine-tuning.