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 context and up-to-date information needed for accurate responses in business settings. Retrieval Augmented Generation (RAG) techniques…

In enterprise environments, organizations often divide their AI operations into two specialized teams: an AI research team and a model hosting team. The research team is dedicated to developing and enhancing AI models using model training and fine-tuning techniques. Meanwhile, a separate hosting team is responsible for deploying these models across their own development, staging,…

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This blog post is co-written with George Orlin from Meta. Today, we are excited to announce that Meta’s Segment Anything Model (SAM) 2.1 vision segmentation model is publicly available through Amazon SageMaker JumpStart to deploy and run inference. Meta SAM 2.1 provides state-of-the-art video and image segmentation capabilities in a single model. This cutting-edge model…

This post was written by Claudiu Bota, Oleg Yurchenko, and Vladyslav Melnyk of AWS Partner Automat-it. As organizations adopt AI and machine learning (ML), they’re using these technologies to improve processes and enhance products. AI use cases include video analytics, market predictions, fraud detection, and natural language processing, all relying on models that analyze data…

In this new era of emerging AI technologies, we have the opportunity to build AI-powered assistants tailored to specific business requirements. Amazon Q Business, a new generative AI-powered assistant, can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in an enterprise’s systems. Large-scale data ingestion is crucial for…