Fine-tuning a pre-trained large language model (LLM) allows users to customize the model to perform better on domain-specific tasks or align more closely with human preferences. It is a continuous process to keep the fine-tuned model accurate and effective...
Large language models (LLMs) excel at generating human-like text but face a critical challenge: hallucination—producing responses that sound convincing but are factually incorrect. While these models are trained on vast amounts of generic data, they often lack the organization-specific...
Generative AI is revolutionizing enterprise automation, enabling AI systems to understand context, make decisions, and act independently. Generative AI foundation models (FMs), with their ability to understand context and make decisions, are becoming powerful partners in solving sophisticated business...
Data is the lifeblood of modern applications, driving everything from application testing to machine learning (ML) model training and evaluation. As data demands continue to surge, the emergence of generative AI models presents an innovative solution. These large language...
This post is co-written with Sajin Jacob, Jerry Chen, Siddarth Mohanram, Luis Barbier, Kristen Chenowith, and Michelle Stahl from Verisk.
Verisk (Nasdaq: VRSK) is a leading data analytics and technology partner for the global insurance industry. Through advanced analytics, software,...
At AWS re:Invent 2024, we launched a new innovation in Amazon SageMaker HyperPod on Amazon Elastic Kubernetes Service (Amazon EKS) that enables you to run generative AI development tasks on shared accelerated compute resources efficiently and reduce costs by...
