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