Generative AI

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How INRIX accelerates transportation planning with Amazon Bedrock

Amazon Bedrock, Amazon Nova, Artificial Intelligence, Customer Solutions, Generative AI, image visualization, Intermediate (200) /

This post is co-written with Shashank Saraogi, Nat Gale, and Durran Kelly from INRIX. The complexity of modern traffic management extends far beyond mere road monitoring, encompassing massive amounts of data collected worldwide from connected cars, mobile devices, roadway sensors, and major event monitoring systems. For transportation authorities managing urban, suburban, and rural traffic flow, […]

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Build a just-in-time knowledge base with Amazon Bedrock

Amazon Bedrock, Amazon Bedrock Knowledge Bases, Cloud Cost Optimization, Generative AI, SaaS /

Software as a service (SaaS) companies managing multiple tenants face a critical challenge: efficiently extracting meaningful insights from vast document collections while controlling costs. Traditional approaches often lead to unnecessary spending on unused storage and processing resources, impacting both operational efficiency and profitability. Organizations need solutions that intelligently scale processing and storage resources based on

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Transforming network operations with AI: How Swisscom built a network assistant using Amazon Bedrock

Amazon Machine Learning, Artificial Intelligence, Customer Solutions, Generative AI /

In the telecommunications industry, managing complex network infrastructures requires processing vast amounts of data from multiple sources. Network engineers often spend considerable time manually gathering and analyzing this data, taking away valuable hours that could be spent on strategic initiatives. This challenge led Swisscom, Switzerland’s leading telecommunications provider, to explore how AI can transform their

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End-to-End model training and deployment with Amazon SageMaker Unified Studio

Advanced (300), AIML, Amazon SageMaker, Amazon SageMaker Unified Studio, Foundation models, Generative AI, Technical How-to /

Although rapid generative AI advancements are revolutionizing organizational natural language processing tasks, developers and data scientists face significant challenges customizing these large models. These hurdles include managing complex workflows, efficiently preparing large datasets for fine-tuning, implementing sophisticated fine-tuning techniques while optimizing computational resources, consistently tracking model performance, and achieving reliable, scalable deployment.The fragmented nature of

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Accelerating AI innovation: Scale MCP servers for enterprise workloads with Amazon Bedrock

Amazon Bedrock, Amazon DynamoDB, Amazon Elastic Container Service, Artificial Intelligence, AWS Fargate, AWS PrivateLink, Elastic Load Balancing, Financial Services, Generative AI, Technical How-to /

Generative AI has been moving at a rapid pace, with new tools, offerings, and models released frequently. According to Gartner, agentic AI is one of the top technology trends of 2025, and organizations are performing prototypes on how to use agents in their enterprise environment. Agents depend on tools, and each tool might have its

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Choosing the right approach for generative AI-powered structured data retrieval

AI/ML, Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Machine Learning, Amazon Q Business, Amazon QuickSight, Amazon Redshift, Amazon SageMaker, Amazon SageMaker AI, Generative AI, Generative BI, Natural Language Processing /

Organizations want direct answers to their business questions without the complexity of writing SQL queries or navigating through business intelligence (BI) dashboards to extract data from structured data stores. Examples of structured data include tables, databases, and data warehouses that conform to a predefined schema. Large language model (LLM)-powered natural language query systems transform how

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Context extraction from image files in Amazon Q Business using LLMs

AI/ML, Amazon Bedrock, Amazon Machine Learning, Amazon Q, Amazon Q Business, Artificial Intelligence, Generative AI, Intermediate (200) /

To effectively convey complex information, organizations increasingly rely on visual documentation through diagrams, charts, and technical illustrations. Although text documents are well-integrated into modern knowledge management systems, rich information contained in diagrams, charts, technical schematics, and visual documentation often remains inaccessible to search and AI assistants. This creates significant gaps in organizational knowledge bases, leading

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Build AWS architecture diagrams using Amazon Q CLI and MCP

AI/ML, Amazon Q Developer, Best Practices, Generative AI, Technical How-to /

Creating professional AWS architecture diagrams is a fundamental task for solutions architects, developers, and technical teams. These diagrams serve as essential communication tools for stakeholders, documentation of compliance requirements, and blueprints for implementation teams. However, traditional diagramming approaches present several challenges: Time-consuming process – Creating detailed architecture diagrams manually can take hours or even days

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AWS costs estimation using Amazon Q CLI and AWS Cost Analysis MCP

Amazon Q Developer, Best Practices, Generative AI, Technical How-to /

Managing and optimizing AWS infrastructure costs is a critical challenge for organizations of all sizes. Traditional cost analysis approaches often involve the following: Complex spreadsheets – Creating and maintaining detailed cost models, which requires significant effort Multiple tools – Switching between the AWS Pricing Calculator, AWS Cost Explorer, and third-party tools Specialized knowledge – Understanding

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Tailor responsible AI with new safeguard tiers in Amazon Bedrock Guardrails

Amazon Bedrock, Amazon Bedrock Guardrails, Artificial Intelligence, Best Practices, Generative AI, Responsible AI, Technical How-to, Thought Leadership /

Amazon Bedrock Guardrails provides configurable safeguards to help build trusted generative AI applications at scale. It provides organizations with integrated safety and privacy safeguards that work across multiple foundation models (FMs), including models available in Amazon Bedrock, as well as models hosted outside Amazon Bedrock from other model providers and cloud providers. With the standalone

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