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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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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

Such a virtual assistant should support users across various business functions, such as finance, legal, human resources, and operations. This architecture workflow includes the following steps: A user submits a question through a web or mobile application. The architecture of this system is illustrated in the following figure.

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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. The solution incorporates the following key features: Using a Retrieval Augmented Generation (RAG) architecture, the system generates a context-aware detailed assessment.

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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

AWS Machine Learning - AI

Solution overview The following architecture diagram represents the high-level design of a solution proven effective in production environments for AWS Support Engineering. The following diagram illustrates an example architecture for ingesting data through an endpoint interfacing with a large corpus.

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Track, allocate, and manage your generative AI cost and usage with Amazon Bedrock

AWS Machine Learning - AI

Although tagging is supported on a variety of Amazon Bedrock resources —including provisioned models, custom models, agents and agent aliases, model evaluations, prompts, prompt flows, knowledge bases, batch inference jobs, custom model jobs, and model duplication jobs—there was previously no capability for tagging on-demand foundation models.

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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. Depending on the use case and data isolation requirements, tenants can have a pooled knowledge base or a siloed one and implement item-level isolation or resource level isolation for the data respectively.