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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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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. The following figure illustrates the high-level design of the solution.

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

AWS Machine Learning - AI

The solution presented in this post takes approximately 15–30 minutes to deploy and consists of the following key components: Amazon OpenSearch Service Serverless maintains three indexes : the inventory index, the compatible parts index, and the owner manuals index. Python 3.9 or later Node.js

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Use AWS Generative AI CDK Constructs To Speed up App Development

Dzone - DevOps

In this blog, we will use the AWS Generative AI Constructs Library to deploy a complete RAG application composed of the following components: Knowledge Bases for Amazon Bedrock : This is the foundation for the RAG solution. An S3 bucket: This will act as the data source for the Knowledge Base.

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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

Users can access these AI capabilities through their organizations single sign-on (SSO), collaborate with team members, and refine AI applications without needing AWS Management Console access. The workflow is as follows: The user logs into SageMaker Unified Studio using their organizations SSO from AWS IAM Identity Center.

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The Future of Serverless is … Functionless?

Stackery

I first heard about this pattern a few years ago at a ServerlessConf from a consultant who was helping a “big bank” convert to serverless. 6.10, which is approaching EOL for AWS Lambda? What if, instead, we could do the following: This may seem magical, but it’s possible using advanced mechanisms built into AWS API Gateway.

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Revolutionize trip planning with Amazon Bedrock and Amazon Location Service

AWS Machine Learning - AI

This is where AWS and generative AI can revolutionize the way we plan and prepare for our next adventure. This innovative service goes beyond traditional trip planning methods, offering real-time interaction through a chat-based interface and maintaining scalability, reliability, and data security through AWS native services.