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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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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.

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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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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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Securing a Web Application with AWS Application Load Balancer

Stackery

Editor’s note: while we love serverless at Stackery, there are still some tasks that will require the use of a virtual machine. If you’re still using an Elastic Compute Cloud (EC2) Virtual Machine, enjoy this very useful tutorial on load balancing. Need to install an image library on the OS layer? Obtain a Certificate.

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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

Generative artificial intelligence (AI) applications are commonly built using a technique called Retrieval Augmented Generation (RAG) that provides foundation models (FMs) access to additional data they didn’t have during training. The post is co-written with Michael Shaul and Sasha Korman from NetApp.

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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.