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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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Creating asynchronous AI agents with Amazon Bedrock

AWS Machine Learning - AI

This post will discuss agentic AI driven architecture and ways of implementing. Agentic AI architecture Agentic AI architecture is a shift in process automation through autonomous agents towards the capabilities of AI, with the purpose of imitating cognitive abilities and enhancing the actions of traditional autonomous agents.

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

AWS Machine Learning - AI

Without a scalable approach to controlling costs, organizations risk unbudgeted usage and cost overruns. This scalable, programmatic approach eliminates inefficient manual processes, reduces the risk of excess spending, and ensures that critical applications receive priority. However, there are considerations to keep in mind.

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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. Alternatively, you can use AWS Lambda and implement your own logic, or use open source tools such as fmeval.