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.

Unlock advanced LLM customization with Snorkel Flow’s new release! Explore flexible data integrations, secure controls, and multimodal support to fine-tune language models for enterprise use. Discover how to leverage images and diverse data types for AI-driven insights.

Snorkel Flow’s new FM-first workflow for building document intelligence applications will get you from demo to production faster than ever.

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

We’re excited to announce Snorkel Custom to help enterprises cross the chasm from flashy chatbot demos to real production AI value.

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.

A customer wanted an llm system for complex contract question answering tasks. We helped them build it—beating the baseline by 64 points.

Snorkel AI will be at Google Cloud Next. The event will feature more than 700 sessions, so we picked five that we think you shouldn’t miss.

Snorkel AI helped a client solve the challenge of social media content filtering quickly and sustainably. Here’s how.

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.

Microsoft infrastructure facilitates Snorkel AI research experiments, including our recent high rank on the AlpacaEval 2.0 LLM leaderboard.

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

Fine-tuned representation models are often the most effective way to boost the performance of AI applications. Learn why.

Enterprise GenAI 2024: applications will likely surge toward production, according to Snorkel AI Enterprise LLM Summit survey results .

Training large language models is a multi-layered stack of processes, each with its unique role and contribution to the model’s performance.

Low-rank adaptation (LoRA) lets data scientists customize GenAI models like LLMs faster than traditional full fine-tuning methods.

LLM distillation isolates task-specific LLM performance and mirrors it in a smaller format—creating faster and cheaper performance.

Snorkel AI CEO Alex Ratner explains his view on the importance of AI in data development and illustrates his position with two case studies.

Snorkel CEO Alex Ratner talks with QBE Ventures’ Alex Taylor about the future of AI, LLMs and multimodal models in the insurance industry.

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).

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.

Snorkel CEO Alex Ratner chatted with Stanford Professor Percy Liang about evaluation in machine learning and in AI generally.