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

AWS Machine Learning - AI

The solution we explore consists of two main components: a Python application for the UI and an AWS deployment architecture for hosting and serving the application securely. The AWS deployment architecture makes sure the Python application is hosted and accessible from the internet to authenticated users. See the README.md

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

AWS Machine Learning - AI

API Gateway is serverless and hence automatically scales with traffic. Load balancer – Another option is to use a load balancer that exposes an HTTPS endpoint and routes the request to the orchestrator. You can use AWS services such as Application Load Balancer to implement this approach.

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Ngrok, a service to help devs deploy sites, services and apps, raises $50M

TechCrunch

An open source package that grew into a distributed platform, Ngrok aims to collapse various networking technologies into a unified layer, letting developers deliver apps the same way regardless of whether they’re deployed to the public cloud, serverless platforms, their own data center or internet of things devices.

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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

By implementing this architectural pattern, organizations that use Google Workspace can empower their workforce to access groundbreaking AI solutions powered by Amazon Web Services (AWS) and make informed decisions without leaving their collaboration tool. In the following sections, we explain how to deploy this architecture.

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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning - AI

That’s where the new Amazon EMR Serverless application integration in Amazon SageMaker Studio can help. In this post, we demonstrate how to leverage the new EMR Serverless integration with SageMaker Studio to streamline your data processing and machine learning workflows.

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

AWS Machine Learning - AI

MaestroQA integrated Amazon Bedrock into their existing architecture using Amazon Elastic Container Service (Amazon ECS). The following architecture diagram demonstrates the request flow for AskAI. The customer interaction transcripts are stored in an Amazon Simple Storage Service (Amazon S3) bucket.

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Responsible AI in action: How Data Reply red teaming supports generative AI safety on AWS

AWS Machine Learning - AI

The following diagram illustrates the solution architecture. This UI directs traffic through an Application Load Balancer (ALB), facilitating seamless user interactions and allowing red team members to explore, interact, and stress-test models in real time.