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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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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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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

However, this method presents trade-offs. However, it also presents some trade-offs. 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. 70B and 8B.

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

AWS Machine Learning - AI

This post presents a solution where you can upload a recording of your meeting (a feature available in most modern digital communication services such as Amazon Chime ) to a centralized video insights and summarization engine. You can invoke Lambda functions from over 200 AWS services and software-as-a-service (SaaS) applications.

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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.

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How BQA streamlines education quality reporting using Amazon Bedrock

AWS Machine Learning - AI

The architecture seamlessly integrates multiple AWS services with Amazon Bedrock, allowing for efficient data extraction and comparison. The following diagram illustrates the solution architecture. The text summarization Lambda function is invoked by this new queue containing the extracted text.