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Building Resilient Public Networking on AWS: Part 4

Xebia

Region Evacuation with static anycast IP approach Welcome back to our comprehensive "Building Resilient Public Networking on AWS" blog series, where we delve into advanced networking strategies for regional evacuation, failover, and robust disaster recovery. Find the detailed guide here.

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Boost Your Productivity with awscurl: Simplifying IAM-Secured API Testing in AWS

Xebia

When you use AWS, you can interact with it through the console, sdk, or cli. You can use it to perform any API call that supports sigv4, but for the majority of services, the AWS cli tool is the best tool for the job. One of the significant advantages of the cloud is that you get a lot of security controls out of the box.

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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

AWS provides a powerful set of tools and services that simplify the process of building and deploying generative AI applications, even for those with limited experience in frontend and backend development. The AWS deployment architecture makes sure the Python application is hosted and accessible from the internet to authenticated users.

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Introducing AWS MCP Servers for code assistants (Part 1)

AWS Machine Learning - AI

Were excited to announce the open source release of AWS MCP Servers for code assistants a suite of specialized Model Context Protocol (MCP) servers that bring Amazon Web Services (AWS) best practices directly to your development workflow. This post is the first in a series covering AWS MCP Servers.

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How to Operationalize Data From Multiple Sources to Deliver Actionable Insights

Speaker: Speakers from SafeGraph, Facteus, AWS Data Exchange, SimilarWeb, and AtScale

Data and analytics specialists from AWS Data Exchange and AtScale will walk through exactly how to blend and operationalize these diverse data external and internal sources. Real-world examples of data initiatives from AWS Data Exchange, SimilarWeb, Facteus, and SafeGraph customers.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

For example, a marketing content creation application might need to perform task types such as text generation, text summarization, sentiment analysis, and information extraction as part of producing high-quality, personalized content. An example is a virtual assistant for enterprise business operations.

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Using Amazon Q Business with AWS HealthScribe to gain insights from patient consultations

AWS Machine Learning - AI

During re:Invent 2023, we launched AWS HealthScribe , a HIPAA eligible service that empowers healthcare software vendors to build their clinical applications to use speech recognition and generative AI to automatically create preliminary clinician documentation.

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