AI

Build an AI-powered product tagging system with Amazon SageMaker serverless model customization

Retail catalogs rarely arrive as clean, structured attributes. Product names, descriptions, and category paths come from many sources and change continuously. Search, recommendations, and catalog navigation depend on consistent tags, but manually applying those tags across thousands of stock keeping units (SKUs) is slow and difficult to keep consistent. A general-purpose frontier model can generate

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Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale

AI agents now run in production at a scale of billions of operations a day, and a recurring architectural pattern has surfaced: agents need a compute scratch pad. Not only for coding tasks, but for data aggregation, analysis, verification, and any workflow where semantic reasoning alone isn’t enough. Abnormal AI, a behavioral security service that

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Manage end-user OAuth consent for AI agents with Amazon Bedrock AgentCore

AI agents often need to access services such as GitHub and Slack on a user’s behalf. Before an agent can act, the user must authenticate with the provider and explicitly approve the requested access. The application must then securely associate the resulting OAuth grant with the user who authorized it. This process is called session

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How Ninth Wave built AI-powered open finance onboarding on Amazon Bedrock

Financial institutions participating in open finance (the network where banks share customer-authorized financial data with third-party applications through standardized APIs) face a persistent integration challenge. Every bank exposes APIs with its own field names, formatting conventions, and gaps relative to the Financial Data Exchange (FDX) standard. Validating those APIs, mapping fields, and scoring readiness for

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The generative AI customization spectrum: From prompt engineering to custom models on AWS

This post shows you how to pick the right generative AI customization approach for your workload without over-engineering or under-investing. AWS provides access to foundation models from Anthropic, Meta, Mistral, and Amazon through Amazon Bedrock, along with the infrastructure to build everything from chatbots and code assistants to document processors and autonomous agents. The models

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Automate replenishment with MMF, Databricks Genie, and Amazon Quick

AWS Machine Learning Blog: technical walkthrough. Commands and expected outputs are from a working deployment. Substitute your own account values throughout. Replenishment automation starts with a demand forecast, and in retail that forecast has a short shelf life. By the time a planner exports it, checks it against supplier availability, and works down tens of

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