Data-Centric AI

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.

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.

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.

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.

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.

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.

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!”

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

QBE Ventures made a strategic investment in Snorkel AI because it provides what Insurers need: scalable and affordable ways to customize AI.

Snorkel CEO Alex Ratner spoke with Douwe Keila, an author of the original paper about retrieval augmented generation (RAG).

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

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

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

LLM distillation will become a more important in 2024, according to a poll of attendees at Snorkel AI’s 2023 Enterprise LLM virtual summit.

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

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

Gideon Mann, head of ML Product and Research at Bloomberg LP, chatted with Snorkel CEO Alex Ratner about building BloombergGPT.

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.

Regulators and compliance officers face a constantly evolving landscape of financial markets. Rule-based systems struggle where AI succeeds.