Data Development

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

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.

In its first six months, Snorkel Foundry collaborated on high-value projects with notable companies and produced impressive results.

When done right, advanced classification applications cultivate business value and automation, unlock new business lines, and reduce costs.

A brief guide on how financial institutions could use Google Dialogflow with Snorkel Flow to build better chatbots for retail banking

A proof-of-concept project that combines predictive AI + generative AI to minimize LLM’s risks while keeping their advantages.

Data scientists can fine-tune Llama 2 to adapt it to specific tasks. The Snorkel Flow data development platform makes it easy to do so.

Large language models open many new opportunities for data science teams, but enterprise LLM challenges persist—and customization is key.

The 2023.R3 Snorkel Flow release is packed with improvements that amplify user experience, streamline workflows, and enhance performance, ensuring our users derive unparalleled value from our platform.

The Biden administration issued an executive order that creates new AI standards and challenges. AI data development can help.

The Snorkel AI team will present 18 research papers and talks at the 2023 Neural Information Processing Systems (NeurIPS) conference from December 10-16. The Snorkel papers cover a broad range of topics including fairness, semi-supervised learning, large language models (LLMs), and domain-specific models. Snorkel AI is proud of its roots in the research community and endeavors to remain at the forefront

Distillation techniques allow enterprises to access the full predictive power of large language models at a tiny fraction of their cost.

Snorkel AI’s Enterprise LLM Virtual Summit drew 1,000 attendees with speakers from Contextual AI, Google, Meta, Stanford, and Together AI.

Insurance claims processing has long required a lot of tedious and expensive human labor, but artificial intelligence (AI) can help.

Snorkel Flow makes it easy to fine tune LLMs like GPT-3.5 Turbo to work better for specific domain and enterprise requirements.

Sessions at the Future of Data-Centric AI covered LLMs, gen AI, and more. All recordings are now publicly available. See them here!

Most data science leaders expect to customize LLMS, but the process of making LLMs work for your business is still a fresh challenge.

Ai and ML offer new avenues for credit scoring solutions and could usher in a new era of fairness, efficiency, and risk management.

Data labeling remains a core requirement for machine learning projects—especially in the age of genAI and LLMs. Here’s a handy guide.