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

AWS Machine Learning - AI

AWS provides a powerful set of tools and services that simplify the process of building and deploying generative AI applications, even for those with limited experience in frontend and backend development. The AWS deployment architecture makes sure the Python application is hosted and accessible from the internet to authenticated users.

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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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How to Use Application Load Balancer and Amazon Cognito to Authenticate Users for Your Kubernetes Web Apps

DevOps.com

This post describes how to use Amazon Cognito to authenticate users for web apps running in an Amazon Elastic Kubernetes Services (Amazon EKS) cluster.

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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 SSL certificate created and imported into AWS Certificate Manager (ACM).

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Securing a Web Application with AWS Application Load Balancer

Stackery

AWS Lambdas don’t let you do that. If you’re still using an Elastic Compute Cloud (EC2) Virtual Machine, enjoy this very useful tutorial on load balancing. I was recently called upon to secure an Nginx web server with HTTPS, and my goal was to set this up with a certificate obtained from AWS Certificate Manager.

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Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector database on AWS Marketplace

AWS Machine Learning - AI

We demonstrate how to build an end-to-end RAG application using Cohere’s language models through Amazon Bedrock and a Weaviate vector database on AWS Marketplace. Additionally, you can securely integrate and easily deploy your generative AI applications using the AWS tools you are already familiar with.

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Build RAG-based generative AI applications in AWS using Amazon FSx for NetApp ONTAP with Amazon Bedrock

AWS Machine Learning - AI

In this post, we demonstrate a solution using Amazon FSx for NetApp ONTAP with Amazon Bedrock to provide a RAG experience for your generative AI applications on AWS by bringing company-specific, unstructured user file data to Amazon Bedrock in a straightforward, fast, and secure way. Install the AWS Command Line Interface (AWS CLI).