Remove Authentication Remove Document Remove Lambda
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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

AWS offers powerful generative AI services , including Amazon Bedrock , which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. This request contains the user’s message and relevant metadata.

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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

AWS Machine Learning - AI

Large-scale data ingestion is crucial for applications such as document analysis, summarization, research, and knowledge management. These tasks often involve processing vast amounts of documents, which can be time-consuming and labor-intensive. It enables end-user authentication and streamlines access management.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

We demonstrate how to harness the power of LLMs to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on AWS Well-Architected best practices. An interactive chat interface allows deeper exploration of both the original document and generated content.

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Serverless, it can help you brew beer

Xebia

This allows you to use a Lambda function to use business logic to decide whether the call can be performed. The documentation clearly states that you should not use the usage plans for authentication. Another other option would be a custom authorizer. In my case, I used the usage plans functionality. And I am not!

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Accenture creates a regulatory document authoring solution using AWS generative AI services

AWS Machine Learning - AI

A key part of the submission process is authoring regulatory documents like the Common Technical Document (CTD), a comprehensive standard formatted document for submitting applications, amendments, supplements, and reports to the FDA. The tedious process of compiling hundreds of documents is also prone to errors.

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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. It contains services used to onboard, manage, and operate the environment, for example, to onboard and off-board tenants, users, and models, assign quotas to different tenants, and authentication and authorization microservices.

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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

Annotators can precisely mark and evaluate specific moments in audio or video content, helping models understand what makes content feel authentic to human viewers and listeners. Pre-annotation and post-annotation AWS Lambda functions are optional components that can enhance the workflow. Give your job a name.

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