Resource library
Evaluating Coding Agent Capabilities with Terminal-Bench: Snorkel’s Role in Building the Next Generation Benchmark
Closing the Evaluation Gap in Agentic AI
Announcing a $3M commitment to launch Open Benchmarks Grants
Benchtalks #1: Alex Shaw (Terminal-Bench, Harbor) – Building the Benchmark Factory
Building FinQA: An Open RL Environment for Financial Reasoning Agents
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.
The medical industry is exploding with data. Manually labeling data for clinical trials is a challenge. Fortunately, AI can help.
Professionals in the data science space often debate whether RAG or fine-tuning yields the better result. The answer is “both.”
We conducted research to reduce the amount of labeled data required to train machine learning systems. The pinnacle of this effort is the development of TAGLETS, a machine learning system that seamlessly integrates widely known collections of labeled data with a diverse array of machine learning algorithms, known as weak labelers. The system’s evolution has been significantly influenced by comprehensive…
Past U.S. Chief Data Scientist DJ Patil talked with Snorkel AI CEO Alex Ratner on topics including the origin of the title “data scientist.”
We designed, implemented, and rolled out a multi-faceted autoscaling solution that expands our ML capabilities while saving on cloud costs.
The surest way to improve foundation models is through more and better data, but Snorkel researchers showed FMs can learn from themselves.
GPT-3 unlocked additional capacity by automating first drafts of internal updates—including blog summaries and sample tweets.
