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
Fine-tuned representation models are often the most effective way to boost the performance of AI applications. Learn why.
Enterprise GenAI 2024: applications will likely surge toward production, according to Snorkel AI Enterprise LLM Summit survey results .
Training large language models is a multi-layered stack of processes, each with its unique role and contribution to the model’s performance.
Low-rank adaptation (LoRA) lets data scientists customize GenAI models like LLMs faster than traditional full fine-tuning methods.
LLM distillation isolates task-specific LLM performance and mirrors it in a smaller format—creating faster and cheaper performance.
In this webinar, you’ll learn how your unique data is critical to developing high-quality generative AI applications and learn where your data can be used and how it should be prepared, managed, and applied to deliver real-world value for your organization.
Snorkel AI CEO Alex Ratner explains his view on the importance of AI in data development and illustrates his position with two case studies.
In this webinar, you’ll learn how your unique data is critical to developing high-quality generative AI applications and learn where your data can be used and how it should be prepared, managed, and applied to deliver real-world value for your organization.
Snorkel CEO Alex Ratner talks with QBE Ventures’ Alex Taylor about the future of AI, LLMs and multimodal models in the insurance industry.
