Remove Authentication Remove Lambda Remove Machine Learning
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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. This request contains the user’s message and relevant metadata.

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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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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. Authentication is performed against the Amazon Cognito user pool.

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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

AWS Machine Learning - AI

The service users permissions are authenticated using IAM Identity Center, an AWS solution that connects workforce users to AWS managed applications like Amazon Q Business. It enables end-user authentication and streamlines access management. The Process Data Lambda function redacts sensitive data through Amazon Comprehend.

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Building a virtual meteorologist using Amazon Bedrock Agents

AWS Machine Learning - AI

Additionally, we use various AWS services, including AWS Amplify for hosting the front end, AWS Lambda functions for handling request logic, Amazon Cognito for user authentication, and AWS Identity and Access Management (IAM) for controlling access to the agent. Use the.zip file to manually deploy the application in Amplify.

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Create generative AI agents that interact with your companies’ systems in a few clicks using Amazon Bedrock in Amazon SageMaker Unified Studio

AWS Machine Learning - AI

Lets look at an example solution for implementing a customer management agent: An agentic chat can be built with Amazon Bedrock chat applications, and integrated with functions that can be quickly built with other AWS services such as AWS Lambda and Amazon API Gateway. For Authentication method , choose API Keys (Max.

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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

Audio and video segmentation provides a structured way to gather this detailed feedback, allowing models to learn through reinforcement learning from human feedback (RLHF) and supervised fine-tuning (SFT). Pre-annotation and post-annotation AWS Lambda functions are optional components that can enhance the workflow.

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