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How Hapag-Lloyd uses Amazon Bedrock to transform customer feedback into actionable insights

How Hapag-Lloyd uses Amazon Bedrock to transform customer feedback into actionable insights

Hapag-Lloyd stands as one of the world’s leading liner shipping companies, operating a modern fleet of 313 container ships with a total transport capacity of 2.5 million TEU (Twenty-foot Equivalent Unit—a standard unit of measurement for cargo capacity in container shipping). The company maintains a container capacity of 3.7 million TEU, which includes one of …

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Streamlining generative AI development with MLflow v3.10 on Amazon SageMaker AI

Streamlining generative AI development with MLflow v3.10 on Amazon SageMaker AI

Today, we’re excited to announce that Amazon SageMaker AI MLflow Apps now support MLflow version 3.10, bringing enhanced capabilities for generative AI development and streamlined experiment tracking to your generative AI workflows. Building on the foundations established with Amazon SageMaker AI MLflow Apps, this latest version introduces powerful new features for observability, evaluation, and generative …

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Introducing OS Level Actions in Amazon Bedrock AgentCore Browser

Introducing OS Level Actions in Amazon Bedrock AgentCore Browser

AI agents that automate web workflows operate within the browser’s web layer, the DOM that Playwright and the Chrome DevTools Protocol (CDP) expose. AgentCore Browser provides a secure, isolated browser environment for this, and it works well for the vast majority of automation: navigating pages, filling forms, clicking elements, extracting content. But the web layer …

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Secure AI agents with Amazon Bedrock AgentCore Identity on Amazon ECS

Secure AI agents with Amazon Bedrock AgentCore Identity on Amazon ECS

AI agents in production require secure access to external services. Amazon Bedrock AgentCore Identity, available as a standalone service, secures how your AI agents access external services whether they run on compute platforms like Amazon ECS, Amazon EKS, AWS Lambda, or on-premises. An earlier post covered AgentCore Identity credential management for AI agents. Running agents …

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Intelligence-driven message defense and insights using Amazon Bedrock

Intelligence-driven message defense and insights using Amazon Bedrock

Direct communication between buyers and sellers outside approved channels can result in significant revenue loss annually while severely damaging brand reputation and destroying valuable business relationships. While messaging systems are essential for modern business operations and help provide rich customer insights, they can create significant risks when parties bypass the brokerage system to communicate directly. …

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Beyond BI: How the Dataset Q&A feature of Amazon Quick powers the next generation of data decisions

Beyond BI: How the Dataset Q&A feature of Amazon Quick powers the next generation of data decisions

Business leaders across industries rely on operational dashboards as the shared source of truth that their teams execute against daily. But dashboards are built to answer known questions. When teams need to explore further, ad-hoc, multi-dimensional, or unforeseen questions, they hit a bottleneck. They wait hours or days for BI teams to build new views …

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Introducing the agent performance loop: AgentCore Optimization now in preview

Introducing the agent performance loop: AgentCore Optimization now in preview

Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were never designed for. Agent quality quietly degrades. In most teams, the improvement …

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Agent-guided workflows to accelerate model customization in Amazon SageMaker AI

Agent-guided workflows to accelerate model customization in Amazon SageMaker AI

Every organization has access to the same foundation models. The real competitive advantage comes from customizing them with your proprietary data and domain expertise. But getting there is complex, even for experienced teams. It requires mastering fine-tuning techniques like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning Verifiable Rewards (RLVR), navigating fragmented APIs …

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