Remove Authentication Remove Load Balancer Remove Resources
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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

In this post, we explore a practical solution that uses Streamlit , a Python library for building interactive data applications, and AWS services like Amazon Elastic Container Service (Amazon ECS), Amazon Cognito , and the AWS Cloud Development Kit (AWS CDK) to create a user-friendly generative AI application with authentication and deployment.

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Deploy Meta Llama 3.1-8B on AWS Inferentia using Amazon EKS and vLLM

AWS Machine Learning - AI

Before running the following commands, make sure you authenticate towards AWS : export AWS_REGION=us-east-1 export CLUSTER_NAME=my-cluster export EKS_VERSION=1.30 Before running the following commands, make sure you authenticate towards AWS : export AWS_REGION=us-east-1 export CLUSTER_NAME=my-cluster export EKS_VERSION=1.30

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

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. You can use AWS services such as Application Load Balancer to implement this approach.

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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. For Authentication Audience , select App URL , as shown in the following screenshot.

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Build a custom UI for Amazon Q Business

AWS Machine Learning - AI

The workflow includes the following steps: The user accesses the chatbot application, which is hosted behind an Application Load Balancer. After the user logs in, they’re redirected to the Amazon Cognito login page for authentication. An IAM role in the account with sufficient permissions to create the necessary resources.

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Revolutionizing customer service: MaestroQA’s integration with Amazon Bedrock for actionable insight

AWS Machine Learning - AI

Originally, they were doing the load balancing themselves, distributing requests between available AWS US Regions ( us-east-1 , us-west-2 , and so on) and available EU Regions ( eu-west-3 , eu-central-1 , and so on) for their North American and European customers, respectively.

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Azure Virtual Machine Tutorial

The Crazy Programmer

Resource group – Here you have to choose a resource group where you want to store the resources related to your virtual machine. Basically resource groups are used to group the resources related to a project. you can think it as a folder containing resources so you can monitor it easily. Management.

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