This post is co-written with Keith Beaudoin and Nicolas Bordeleau from Coveo.
As generative AI transforms business operations, enterprises face a critical challenge: how can they help large language models (LLMs) provide accurate and trustworthy responses? Without reliable data foundations, these AI models can generate...
Training and deploying large AI models requires advanced distributed computing capabilities, but managing these distributed systems shouldn’t be complex for data scientists and machine learning (ML) practitioners. The newly released command line interface (CLI) and software development kit (SDK) for Amazon SageMaker HyperPod simplify...
As organizations increasingly adopt foundation models (FMs) for their artificial intelligence and machine learning (AI/ML) workloads, managing large-scale inference operations efficiently becomes crucial. Amazon Bedrock supports two general types of large-scale inference patterns: real-time inference and batch inference for use cases that involve processing...
Today, Amazon SageMaker HyperPod is announcing a new one-click, validated cluster creation experience that accelerates setup and prevents common misconfigurations, so you can launch your distributed training and inference clusters complete with Slurm or Amazon Elastic Kubernetes Service (Amazon EKS) orchestration, Amazon Virtual Private...
Intelligent document processing (IDP) refers to the automated extraction, classification, and processing of data from various document formats—both structured and unstructured. Within the IDP landscape, key information extraction (KIE) serves as a fundamental component, enabling systems to identify and extract critical data points from...
Retrieval Augmented Generation (RAG) is a powerful approach for building generative AI applications by providing foundation models (FMs) access to additional, relevant data. This approach improves response accuracy and transparency while avoiding the potential cost and complexity of FM training or fine-tuning.
Many customers use...
