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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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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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Deploy Meta Llama 3.1-8B on AWS Inferentia using Amazon EKS and vLLM

AWS Machine Learning - AI

We also demonstrate how to test the solution and monitor performance, and discuss options for scaling and multi-tenancy. Test the deployment After you deploy the model, it’s important to monitor its progress and verify its readiness. Startup probe – Gives the application time to start up. Set up the Inferentia 2 node group.

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One Year of Load Balancing

Algolia

From the beginning at Algolia, we decided not to place any load balancing infrastructure between our users and our search API servers. An Algolia application runs on top of the following infrastructure components: a cluster of 3 servers which process both indexing and search queries, some DSNs servers (not DNS).

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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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Test drive the Citus 11.0 beta for Postgres

The Citus Data

The easiest way to use Citus is to connect to the coordinator node and use it for both schema changes and distributed queries, but for very demanding applications, you now have the option to load balance distributed queries across the worker nodes in (parts of) your application by using a different connection string and factoring a few limitations.

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Koyeb is a serverless platform that integrates with your GitHub repository

TechCrunch

It has already been tested by 10,000 developers during the private beta phase. There are currently 3,000 applications running on Koyeb’s infrastructure. In that case, Koyeb launches your app on several new instances and traffic is automatically load balanced between those instances.