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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Software-as-a-service (SaaS) applications with tenant tiering SaaS applications are often architected to provide different pricing and experiences to a spectrum of customer profiles, referred to as tiers. The user prompt is then routed to the LLM associated with the task category of the reference prompt that has the closest match.

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Transcribe, translate, and summarize live streams in your browser with AWS AI and generative AI services

AWS Machine Learning - AI

Recognizing this need, we have developed a Chrome extension that harnesses the power of AWS AI and generative AI services, including Amazon Bedrock , an AWS managed service to build and scale generative AI applications with foundation models (FMs). The user signs in by entering a user name and a password.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. This allows teams to focus more on implementing improvements and optimizing AWS infrastructure. This systematic approach leads to more reliable and standardized evaluations.

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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. Shared components refer to the functionality and features shared by all tenants. You can use AWS services such as Application Load Balancer to implement this approach. API Gateway also provides a WebSocket API.

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Serving LLMs using vLLM and Amazon EC2 instances with AWS AI chips

AWS Machine Learning - AI

Using vLLM on AWS Trainium and Inferentia makes it possible to host LLMs for high performance inference and scalability. Deploy vLLM on AWS Trainium and Inferentia EC2 instances In these sections, you will be guided through using vLLM on an AWS Inferentia EC2 instance to deploy Meta’s newest Llama 3.2 You will use inf2.xlarge

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Orchestrate generative AI workflows with Amazon Bedrock and AWS Step Functions

AWS Machine Learning - AI

This post discusses how to use AWS Step Functions to efficiently coordinate multi-step generative AI workflows, such as parallelizing API calls to Amazon Bedrock to quickly gather answers to lists of submitted questions. We're more than happy to provide further references upon request.

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How AWS sales uses Amazon Q Business for customer engagement

AWS Machine Learning - AI

Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI. Field Advisor serves four primary use cases: AWS-specific knowledge search With Amazon Q Business, weve made internal data sources as well as public AWS content available in Field Advisors index.

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