📰 AWS Machine Learning

Serving automatic speech recognition (ASR) models at scale is costly when each request uses only a fraction of a GPU. Learn how NVIDIA CUDA Multi-Process Servic

Amazon SageMaker HyperPod now offers managed Ray support on Amazon EKS. Create and monitor Ray clusters, connect JupyterLab and Code Editor notebooks to live cl

Classifying, extracting, and validating high volumes of documents is a challenge across banking, insurance, healthcare, and the public sector. See how a mid-siz

In multi-turn reinforcement learning, your custom reward function decides what the model actually learns. This post shows how to design a composite multi-turn r

The AWS Generative AI Innovation Center built an automated system that uses constraint programming and custom tree search to determine, with mathematical certai

This post walks through deploying Kimi K3 on AWS using two approaches: Amazon SageMaker HyperPod, and  Amazon Elastic Kubernetes Service (Amazon EKS) clust

This post walks through deploying Kimi K3 on AWS using two approaches: Amazon SageMaker HyperPod, and  Amazon Elastic Kubernetes Service (Amazon EKS) clust

Learn how to build an inference meta-monitoring system for Amazon SageMaker AI endpoints using Amazon Quick. This governance layer sits above production ML infe

In this post, we explore how Guardoc Health uses the Amazon Nova family of models, available through Amazon Bedrock, to transform clinical documentation in long

Learn the architecture and design decisions behind an explainable next-best-product recommendation system for banking, built with Amazon SageMaker AI and PyTorc

In this post, we present a solution designed to centralize the monitoring of SageMaker Pipelines across AWS accounts and Regions using Amazon CloudWatch custom

In this post, you will learn four deployment patterns for taking models that have already been quantized with Unsloth and deploying them on AWS infrastructure.

In this post, we show where MCP tool design goes wrong and how to fix it with practical context engineering approaches.

In this post, we walk through five capabilities now available in SageMaker HyperPod inference: multi-tier data capture for auditing and model improvement, direc

Today, we're announcing the Claude apps gateway for AWS, a self-hosted control plane that gives organizations a single point of control over access, cost, and p

Implementing a data and model monitoring solution is necessary to maintain prediction accuracy and help achieve the best outcome for your machine learning use c

In this post, you deploy a two-phase infrastructure for multi-turn RL using Amazon Nova Forge on Amazon SageMaker HyperPod. By the end, you have an event-driven

In this post, you learn how to use the new MLflow integration with Amazon SageMaker AI optimized inference recommendation jobs and Amazon SageMaker AI benchmark

In this post, we share best practices for reliable multi-turn RL training. We cover how to build a training environment you can trust, set up an external evalua

The Amazon Bedrock Model Profiler is an open source tool that aggregates model metadata from multiple AWS APIs and external sources into a single, searchable in

In this post, you’ll build a server that extracts text from PDF files in Amazon S3 in real time. This protocol-based approach provides programmatic document acc

In this post, we walk through how Huntington built a scalable AWS solution to detect and redact Personally Identifiable Information (PII) and Payment Card Indus

Amazon SageMaker AI provides fully managed real-time inference hosting for machine learning models. You deploy a model to a SageMaker endpoint backed by one or

Today, we’re announcing inline payload support for Amazon SageMaker AI Async Inference. Customers can now send inference payloads directly in the request body o