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.
Snorkel AI CEO Alex Ratner explains his view on the importance of AI in data development and illustrates his position with two case studies.
Snorkel CEO Alex Ratner talks with QBE Ventures’ Alex Taylor about the future of AI, LLMs and multimodal models in the insurance industry.
We’ve developed new approaches to scale human preferences and align LLM output to enterprise users’ expectations by magnifying SME impact.
Enterprises that aim to build valuable GenAI applications must view them from a systems-level. LLMs are just one part of an ecosystem.
Snorkel AI’s Jan. 25 Enterprise LLM Summit focused on one theme: AI data development drives enterprise AI success.
