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...
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...
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...
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...
In this post, we explore how natural language database analytics can revolutionize the way organizations interact with their structured data through the power of large language model (LLM) agents. Natural language interfaces to databases have long been a goal...
This post was co-written with Nick Frichette and Vijay George from Datadog.
As organizations increasingly adopt Amazon Bedrock for generative AI applications, protecting against misconfigurations that could lead to data leaks or unauthorized model access becomes critical. The AWS Generative...
