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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. This strategy results in more robust, versatile, and efficient applications that better serve diverse user needs and business objectives. In this post, we provide an overview of common multi-LLM applications.

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

AWS Machine Learning - AI

The workflow includes the following steps: The process begins when a user sends a message through Google Chat, either in a direct message or in a chat space where the application is installed. Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message.

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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning - AI

Recently, we’ve been witnessing the rapid development and evolution of generative AI applications, with observability and evaluation emerging as critical aspects for developers, data scientists, and stakeholders. In this post, we set up the custom solution for observability and evaluation of Amazon Bedrock applications.

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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning - AI

Although the principles discussed are applicable across various industries, we use an automotive parts retailer as our primary example throughout this post. A web application serves as the frontend interface where users can initiate parts lookup requests. A user interacts with the Car Parts Agent through a web application interface.

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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning - AI

Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. Building a generative AI application SageMaker Unified Studio offers tools to discover and build with generative AI.

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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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Why Object Storage is Best for Cloud-Native Apps

DevOps.com

A crucial question that plagues cloud application developers is, “What kind of storage should we use for our app?” Unlike other choices like compute runtimes—Lambda/serverless, containers or virtual machines—data storage choice is highly sticky and makes future application improvements and migrations much harder.

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