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
What is AI data development? AI data development includes any action taken to convert raw information into a format useful to AI.
In this webinar, we’ll explain how enterprise AI/ML teams use Snorkel Flow’s evaluation framework to measure GenAI performance based on SME guidelines and feedback, identify areas of improvement and take corrective action to reach production accuracy requirements.
In this demo, learn how Snorkel AI powers efficient data labeling and management for LLM fine-tuning, natively integrating with Amazon Bedrock and SageMaker for seamless, end-to-end development of specialized models.
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.
We aim to help our customers get GenAI into production. In our 2024.R3 release, we’ve delivered some exciting GenAI evaluation results.
Discover new NLP features in Snorkel Flow\’s 2024.R3 release, including named entity recognition for PDFs + advanced sequence tagging tools.
Discover the latest enterprise readiness features for Snorkel Flow. Configure safeguards for data compliance and security.
Existing ML benchmarks lack the depth and diversity of annotations needed for evaluating models on business process management (BPM) tasks. BPM is the practice of documenting, measuring, improving, and automating enterprise workflows. However, research has focused almost exclusively on one task– full end-to-end automation using agents based on multimodal foundation models (FMs) like GPT-4. This focus on automation ignores the…
