Author name: aiepicentre

Detect Amazon Bedrock misconfigurations with Datadog Cloud Security

AI/ML, Amazon Bedrock, Amazon Bedrock Agents, Amazon Bedrock Guardrails, Generative AI, Monitoring and observability, Partner solutions, Security /

This post was co-written with Nick Frichette and Vijay George from Datadog.  As organizations increasingly adopt Amazon Bedrock for generative AI applications, protecting against misconfigurations that could lead to data leaks or unauthorized model access becomes critical. The AWS Generative AI Adoption Index, which surveyed 3,739 senior IT decision-makers across nine countries, revealed that 45% […]

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Set up custom domain names for Amazon Bedrock AgentCore Runtime agents

Amazon Bedrock, Amazon Bedrock Agents, Amazon Machine Learning /

When deploying AI agents to Amazon Bedrock AgentCore Runtime (currently in preview), customers often want to use custom domain names to create a professional and seamless experience. By default, AgentCore Runtime agents use endpoints like https://bedrock-agentcore.{region}.amazonaws.com/runtimes/{EncodedAgentARN}/invocations. In this post, we discuss how to transform these endpoints into user-friendly custom domains (like https://agent.yourcompany.com) using Amazon CloudFront

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Introducing auto scaling on Amazon SageMaker HyperPod

Amazon SageMaker HyperPod, Announcements /

Today, we’re excited to announce that Amazon SageMaker HyperPod now supports managed node automatic scaling with Karpenter, so you can efficiently scale your SageMaker HyperPod clusters to meet your inference and training demands. Real-time inference workloads require automatic scaling to address unpredictable traffic patterns and maintain service level agreements (SLAs). As demand spikes, organizations must

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Meet Boti: The AI assistant transforming how the citizens of Buenos Aires access government information with Amazon Bedrock

Amazon Bedrock, Artificial Intelligence, Customer Solutions, Generative AI /

This post is co-written with Julieta Rappan, Macarena Blasi, and María Candela Blanco from the Government of the City of Buenos Aires. The Government of the City of Buenos Aires continuously works to improve citizen services. In February 2019, it introduced an AI assistant named Boti available through WhatsApp, the most widely used messaging service

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Empowering air quality research with secure, ML-driven predictive analytics

Advanced (300), Amazon SageMaker Canvas, AWS Cloud Development Kit, Nonprofit, Serverless, Technical How-to /

Air pollution remains one of Africa’s most pressing environmental health crises, causing widespread illness across the continent. Organizations like sensors.AFRICA have deployed hundreds of air quality sensors to address this challenge, but face a critical data problem: significant gaps in PM2.5 (particulate matter with diameter less than or equal to 2.5 micrometers) measurement records because

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How Amazon Finance built an AI assistant using Amazon Bedrock and Amazon Kendra to support analysts for data discovery and business insights

Amazon Bedrock, Amazon Kendra, Artificial Intelligence, Customer Solutions /

Finance analysts across Amazon Finance face mounting complexity in financial planning and analysis processes. When working with vast datasets spanning multiple systems, data lakes, and business units, analysts encounter several critical challenges. First, they spend significant time manually browsing data catalogs and reconciling data from disparate sources, leaving less time for valuable analysis and insight

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Mercury foundation models from Inception Labs are now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart

Amazon Bedrock, Amazon SageMaker JumpStart, Announcements /

Today, we are excited to announce that Mercury and Mercury Coder foundation models (FMs) from Inception Labs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can deploy the Mercury FMs to build, experiment, and responsibly scale your generative AI applications on AWS. In this post, we demonstrate how to

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Learn how Amazon Health Services improved discovery in Amazon search using AWS ML and gen AI

Amazon Athena, Amazon Bedrock, Amazon EMR, Amazon SageMaker, AWS Health, Customer Solutions, Healthcare /

Healthcare discovery on ecommerce domains presents unique challenges that traditional product search wasn’t designed to handle. Unlike searching for books or electronics, healthcare queries involve complex relationships between symptoms, conditions, treatments, and services, requiring sophisticated understanding of medical terminology and customer intent. This challenge became particularly relevant for Amazon as we expanded beyond traditional ecommerce

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Enhance Geospatial Analysis and GIS Workflows with Amazon Bedrock Capabilities

Amazon Bedrock, Artificial Intelligence, Intermediate (200) /

As data becomes more abundant and information systems grow in complexity, stakeholders need solutions that reveal quality insights. Applying emerging technologies to the geospatial domain offers a unique opportunity to create transformative user experiences and intuitive workstreams for users and organizations to deliver on their missions and responsibilities. In this post, we explore how you

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Beyond the basics: A comprehensive foundation model selection framework for generative AI

Amazon Bedrock, Artificial Intelligence, Best Practices, Foundation models, Generative AI, Thought Leadership /

Most organizations evaluating foundation models limit their analysis to three primary dimensions: accuracy, latency, and cost. While these metrics provide a useful starting point, they represent an oversimplification of the complex interplay of factors that determine real-world model performance. Foundation models have revolutionized how enterprises develop generative AI applications, offering unprecedented capabilities in understanding and

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