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

AWS Machine Learning - AI

The workflow includes the following steps: Documents (owner manuals) are uploaded to an Amazon Simple Storage Service (Amazon S3) bucket. The Lambda function runs the database query against the appropriate OpenSearch Service indexes, searching for exact matches or using fuzzy matching for partial information.

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

AWS Machine Learning - AI

Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message. After it’s authenticated, the request is forwarded to another Lambda function that contains our core application logic. The code runs in a Lambda function. Implement your business logic in this file.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Careful model selection, fine-tuning, configuration, and testing might be necessary to balance the impact of latency and cost with the desired classification accuracy. Based on the classifier LLMs decision, the Lambda function routes the question to the appropriate downstream LLM, which will generate an answer and return it to the user.

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Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning - AI

Providing recommendations for follow-up assessments, diagnostic tests, or specialist consultations. Data consolidation The transcribed patient reports are consolidated into a structured database, enabling efficient storage, retrieval, and analysis. The audio is converted into text, providing accurate and verbatim transcripts.

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

AWS Machine Learning - AI

The solution also uses Amazon Cognito user pools and identity pools for managing authentication and authorization of users, Amazon API Gateway REST APIs, AWS Lambda functions, and an Amazon Simple Storage Service (Amazon S3) bucket. To launch the solution in a different Region, change the aws_region parameter accordingly.

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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. The generative AI playground is a UI provided to tenants where they can run their one-time experiments, chat with several FMs, and manually test capabilities such as guardrails or model evaluation for exploration purposes.

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Enhance speech synthesis and video generation models with RLHF using audio and video segmentation in Amazon SageMaker

AWS Machine Learning - AI

At its core, Amazon Simple Storage Service (Amazon S3) serves as the secure storage for input files, manifest files, annotation outputs, and the web UI components. Pre-annotation and post-annotation AWS Lambda functions are optional components that can enhance the workflow. On the SageMaker console, choose Create labeling job.

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