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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

Traditionally, building frontend and backend applications has required knowledge of web development frameworks and infrastructure management, which can be daunting for those with expertise primarily in data science and machine learning. For more information on how to manage model access, see Access Amazon Bedrock foundation models.

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Koyeb is a serverless platform that integrates with your GitHub repository

TechCrunch

The company is still focused on serverless infrastructure. But it now offers a general purpose serverless platform that you can configure through a simple “git push” command or by using Docker containers. There are currently 3,000 applications running on Koyeb’s infrastructure.

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

AWS Machine Learning - AI

The workflow includes the following steps: The process begins when a user sends a message through Google Chat, either in a direct message or in a chat space where the application is installed. After it’s authenticated, the request is forwarded to another Lambda function that contains our core application logic.

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

AWS Machine Learning - AI

While organizations continue to discover the powerful applications of generative AI , adoption is often slowed down by team silos and bespoke workflows. Generative AI components provide functionalities needed to build a generative AI application. Each tenant has different requirements and needs and their own application stack.

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

AWS Machine Learning - AI

From deriving insights to powering generative artificial intelligence (AI) -driven applications, the ability to efficiently process and analyze large datasets is a vital capability. That’s where the new Amazon EMR Serverless application integration in Amazon SageMaker Studio can help.

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How to Deploy Tomcat App using AWS ECS Fargate with Load Balancer

Perficient

Amazon Elastic Container Service (ECS): It is a highly scalable, high-performance container management service that supports Docker containers and allows to run applications easily on a managed cluster of Amazon EC2 instances. Before that let’s create a load balancer by performing the following steps.