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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. It has already been tested by 10,000 developers during the private beta phase.

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

AWS Machine Learning - AI

However, as exciting as these advancements are, data scientists often face challenges when it comes to developing UIs and to prototyping and interacting with their business users. With Streamlit, you can quickly build and iterate on your application without the need for extensive frontend development experience.

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

TechCrunch

Ask Alan Shreve why he founded Ngrok , a service that helps developers share sites and apps running on their local machines or servers, and he’ll tell you it was to solve a tough-to-grok (pun fully intended) infrastructure problem he encountered while at Twilio. “Ngrok allows developers to avoid that complexity.”

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

AWS Machine Learning - AI

Responsible AI components promote the safe and responsible development of AI across tenants. 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.

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

AWS Machine Learning - AI

You can customize this architecture to connect other solutions that you develop in AWS to Google Chat. Prerequisites To implement the solution outlined in this post, you must have the following: A Linux or MacOS development environment with at least 20 GB of free disk space. Docker installed on your development environment.

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

AWS Machine Learning - AI

After the data is transcribed, MaestroQA uses technology they have developed in combination with AWS services such as Amazon Comprehend to run various types of analysis on the customer interaction data. Consequently, MaestroQA had to develop a solution capable of scaling to meet their clients extensive needs.