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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. The AWS CDK. Docker or Colima.

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

AWS Machine Learning - AI

The generative AI playground is a UI provided to tenants where they can run their one-time experiments, chat with several FMs, and manually test capabilities such as guardrails or model evaluation for exploration purposes. You can use AWS services such as Application Load Balancer to implement this approach.

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Optimize hosting DeepSeek-R1 distilled models with Hugging Face TGI on Amazon SageMaker AI

AWS Machine Learning - AI

Model Variants The current DeepSeek model collection consists of the following models: DeepSeek-V3 An LLM that uses a Mixture-of-Experts (MoE) architecture. These models retain their existing architecture while gaining additional reasoning capabilities through a distillation process. deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

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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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What is the Difference between Network Architecture and Application Architecture?

The Crazy Programmer

When you are planning to build your network, there is a possibility you may come across two terms “Network Architecture and Application Architecture.” In today’s blog, we will look at the difference between network architecture and application architecture in complete detail.

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

TechCrunch

Effectively, Ngrok adds connectivity, security and observability features to existing apps without requiring any code changes, including features like load balancing and encryption. With Ngrok, developers can deploy or test apps against a development backend, building demo websites without having to deploy them.

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