Resource library
Explore our complete library of resources including blogs, benchmarks, research papers, and more.
Data 2.0 and the research era of AI data
Announcing Snorkel’s $350M fundraise to build the frontier lab for AI data.
Terminal-Bench 4.0: Why Continuous Benchmarks Require Continuous QA
Grok 4.5 Testing Results: How SpaceXAI’s New Model Performs on Real Professional Work
Why coding agents need better data, evals, and environments
Closing the Evaluation Gap in Agentic AI
Announcing a $3M commitment to launch Open Benchmarks Grants
Why Frontier Agents Fail Real Engineering Work: Two Terminal-Bench 3.0 Task Deep Dives
Building FinQA: An Open RL Environment for Financial Reasoning Agents
The science of rubric design
Using Snorkel Flow, Pixability has created a way to build classifiers for massive amounts of YouTube data quickly—that was previously out of reach.
Sirisha Rella, Technical Product Marketing Manager at Nvidia, recently gave a Lightning Talk presentation on “demystifying” speech AI at Snorkel AI’s Future of Data-Centric AI virtual conference.
Snorkel AI will hold a free Foundation Model Virtual Summit on Tuesday, January 17 where speakers from across the technology industry, including some from Google and Stanford University, will discuss the enterprise use of Foundation Models.
Snorkel Flow debuts a new integration with Microsoft Azure Form Recognizer to help organizations leverage Azure AI services.
Researcher Simran Arora tells Snorkel CEO Alex Ratner how she improved foundation model effectiveness by using “Ask Me Anything”-style questions.
See what’s in our latest Snorkel Flow release and how we’re accelerating data-centric AI development further.
More components in an ML lifecycle are designed to run on autopilot, but some tasks require human-in-the-loop ML, an active research topic that has seen an increasing number of publications in the last 10 years.
A central innovation team at a top US bank wanted to modernize its AI development and data annotation processes in order to create a custom natural language processing (NLP) model that could extract important financial information from 10-Ks. Manually reviewing these documents was taking up valuable time that could be better spent assisting customers. The team used Snorkel Flow’s data-centric AI development process and programmatic labeling to train a customized NLP model that could accurately extract information on interest rate swaps.
