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If you’re managing a real-time feature store, you might be facing challenges such as data duplication, feature engineering, feature consistency, manual deployment, and latency. Jumio is an identity verification provider that helps businesses detect fraud and build digital trust. To provide these services in real time, Jumio’s machine learning (ML) models needed a real-time feature […]

Enterprises rely on large volumes of complex legal agreements to make critical business decisions — determining rights, renewal options, geographic restrictions, and compliance obligations. In industries like entertainment and media, where organizations manage thousands of contracts across multiple jurisdictions, this work remains largely manual: time-consuming, costly, and difficult to scale. Our AI-Driven Annotation (AIDA) solution,

Independent Software Vendors (ISVs) are expanding their offerings and adding AI agents. Common considerations for organizations when adding agentic workloads include security, scalability, time to market, and cost tracking. ISVs have another dimension, they provide services to other organizations and need to manage agentic workloads for each customer. As such, ISVs need to manage those

NVIDIA Nemotron 3.5 Lightning is designed for the fast, specialized model execution required by high-volume agentic workloads. With NVIDIA Nemotron 3.5 Lightning on Amazon SageMaker JumpStart, you can access an open model designed for high-volume agentic workloads. With this launch, you can deploy Nemotron 3.5 Lightning from Amazon SageMaker JumpStart without configuring the serving infrastructure

This post is a collaboration between AWS and the OpenClaw Foundation. Autonomous agents that browse the web, call APIs, and query Model Context Protocol (MCP) servers can encounter services that require an HTTP 402 Payment Required response to be settled before access continues. To use those services without pausing for a human at every transaction,

Google Gemini and Pixel partner with five global football clubs to elevate the fan matchday experience through AI and Smartphone Technology. ​ 

In multi-turn reinforcement learning (RL), your custom reward function decides what the model actually learns. A subtly wrong reward can quietly teach the wrong thing while every training curve looks healthy. Designing a reward that holds up over multi-turn, agentic tasks is one of the hardest parts of customizing Amazon Nova models. For multi-turn training,

A common challenge in building agentic workflows is mixing managed foundation models (FMs) with your own cost-optimized or domain-specific models, without rewriting your agent framework to do it. In this post, we show you how to combine OpenAI-compatible endpoints on Amazon SageMaker AI with Amazon Bedrock AgentCore runtime, a capability of Amazon Bedrock AgentCore, and

Sheets canvas turns data into interactive dashboards, custom study trackers, seating charts, and more, all with a simple prompt. ​ 

When you deploy AI agents built with frameworks like Strands Agents, LangGraph, and CrewAI, you need observability into their performance. This holds true whether they run on Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Container Service (Amazon ECS), AWS Lambda, on-premises, or another cloud provider such as Google Cloud Platform (GCP) or Microsoft Azure.