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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

By implementing this architectural pattern, organizations that use Google Workspace can empower their workforce to access groundbreaking AI solutions powered by Amazon Web Services (AWS) and make informed decisions without leaving their collaboration tool. This request contains the user’s message and relevant metadata.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements. In contrast, more complex questions might require the application to summarize a lengthy dissertation by performing deeper analysis, comparison, and evaluation of the research results.

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Automate Amazon Bedrock batch inference: Building a scalable and efficient pipeline

AWS Machine Learning - AI

To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. We walk you through our solution, detailing the core logic of the Lambda functions. Amazon S3 invokes the {stack_name}-create-batch-queue-{AWS-Region} Lambda function.

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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning - AI

We walk through the key components and services needed to build the end-to-end architecture, offering example code snippets and explanations for each critical element that help achieve the core functionality. Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can also bring your own customized models and deploy them to Amazon Bedrock for supported architectures. If it leads to better performance, your existing default prompt in the application is overridden with the new one.

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Creating asynchronous AI agents with Amazon Bedrock

AWS Machine Learning - AI

This post will discuss agentic AI driven architecture and ways of implementing. These AI agents have demonstrated remarkable versatility, being able to perform tasks ranging from creative writing and code generation to data analysis and decision support.

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Building Resilient Public Networking on AWS: Part 4

Xebia

Architecture Overview The accompanying diagram visually represents our infrastructure’s architecture, highlighting the relationships between key components. We will also see how this new method can overcome most of the disadvantages we identified with the previous approach. Without further ado, let’s get into the business!

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