Artificial Intelligence
Category: Artificial Intelligence
Introducing Visa Intelligent Commerce on AWS: Enabling agentic commerce with Amazon Bedrock AgentCore
In this post, we explore how AWS and Visa are partnering to enable agentic commerce through Visa Intelligent Commerce using Amazon Bedrock AgentCore. We demonstrate how autonomous AI agents can transform fragmented shopping and travel experiences into seamless, end-to-end workflows—from discovery and comparison to secure payment authorization—all driven by natural language.
Move Beyond Chain-of-Thought with Chain-of-Draft on Amazon Bedrock
This post explores Chain-of-Draft (CoD), an innovative prompting technique introduced in a Zoom AI Research paper Chain of Draft: Thinking Faster by Writing Less, that revolutionizes how models approach reasoning tasks. While Chain-of-Thought (CoT) prompting has been the go-to method for enhancing model reasoning, CoD offers a more efficient alternative that mirrors human problem-solving patterns—using concise, high-signal thinking steps rather than verbose explanations.
Deploy Mistral AI’s Voxtral on Amazon SageMaker AI
In this post, we demonstrate hosting Voxtral models on Amazon SageMaker AI endpoints using vLLM and the Bring Your Own Container (BYOC) approach. vLLM is a high-performance library for serving large language models (LLMs) that features paged attention for improved memory management and tensor parallelism for distributing models across multiple GPUs.
Enhance document analytics with Strands AI Agents for the GenAI IDP Accelerator
To address the need for businesses to quickly analyze information and unlock actionable insights, we are announcing Analytics Agent, a new feature that is seamlessly integrated into the GenAI IDP Accelerator. With this feature, users can perform advanced searches and complex analyses using natural language queries without SQL or data analysis expertise. In this post, we discuss how non-technical users can use this tool to analyze and understand the documents they have processed at scale with natural language.
Build a multimodal generative AI assistant for root cause diagnosis in predictive maintenance using Amazon Bedrock
In this post, we demonstrate how to implement a predictive maintenance solution using Foundation Models (FMs) on Amazon Bedrock, with a case study of Amazon’s manufacturing equipment within their fulfillment centers. The solution is highly adaptable and can be customized for other industries, including oil and gas, logistics, manufacturing, and healthcare.
Introducing SOCI indexing for Amazon SageMaker Studio: Faster container startup times for AI/ML workloads
Today, we are excited to introduce a new feature for SageMaker Studio: SOCI (Seekable Open Container Initiative) indexing. SOCI supports lazy loading of container images, where only the necessary parts of an image are downloaded initially rather than the entire container.
Build and deploy scalable AI agents with NVIDIA NeMo, Amazon Bedrock AgentCore, and Strands Agents
This post demonstrates how to use the powerful combination of Strands Agents, Amazon Bedrock AgentCore, and NVIDIA NeMo Agent Toolkit to build, evaluate, optimize, and deploy AI agents on Amazon Web Services (AWS) from initial development through production deployment.
Bi-directional streaming for real-time agent interactions now available in Amazon Bedrock AgentCore Runtime
In this post, you will learn about bi-directional streaming on AgentCore Runtime and the prerequisites to create a WebSocket implementation. You will also learn how to use Strands Agents to implement a bi-directional streaming solution for voice agents.
Tracking and managing assets used in AI development with Amazon SageMaker AI
In this post, we’ll explore the new capabilities and core concepts that help organizations track and manage models development and deployment lifecycles. We will show you how the features are configured to train models with automatic end-to-end lineage, from dataset upload and versioning to model fine-tuning, evaluation, and seamless endpoint deployment.
Track machine learning experiments with MLflow on Amazon SageMaker using Snowflake integration
In this post, we demonstrate how to integrate Amazon SageMaker managed MLflow as a central repository to log these experiments and provide a unified system for monitoring their progress.









