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

AWS Machine Learning - AI

The emergence of generative AI has ushered in a new era of possibilities, enabling the creation of human-like text, images, code, and more. Set up your development environment To get started with deploying the Streamlit application, you need access to a development environment with the following software installed: Python version 3.8

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

AWS Machine Learning - AI

there is an increasing need for scalable, reliable, and cost-effective solutions to deploy and serve these models. The account ID and Region are dynamically set using AWS CLI commands, making the process more flexible and avoiding hard-coded values. As a result, traffic won’t be balanced across all replicas of your deployment.

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

AWS Machine Learning - AI

In the first part of the series, we showed how AI administrators can build a generative AI software as a service (SaaS) gateway to provide access to foundation models (FMs) on Amazon Bedrock to different lines of business (LOBs). You can use AWS services such as Application Load Balancer to implement this approach.

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Building Resilient Public Networking on AWS: Part 2

Xebia

Region Evacuation with DNS approach: At this point, we will deploy the previous web server infrastructure in several regions, and then we will start reviewing the DNS-based approach to regional evacuation, leveraging the power of AWS Route 53. You can find the corresponding code for this blog post here.

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SaaS Platfrom Development – How to Start

Existek

The global SaaS market is surging forward due to increasing benefits and is expected to reach a volume of $793bn by 2029. Knowing your project needs and tech capabilities results in great scalability, constant development speed, and long-term viability: Backend: Technologies like Node.js Frontend: Angular, React, or Vue.js

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

AWS Machine Learning - AI

Amazon SageMaker AI provides a managed way to deploy TGI-optimized models, offering deep integration with Hugging Faces inference stack for scalable and cost-efficient LLM deployment. The following code shows how to deploy the DeepSeek-R1-Distill-Llama-8B model to a SageMaker endpoint, directly from the Hugging Face Hub.

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Why you must extend Zero Trust to public cloud workloads

CIO

1 The rapid migration to the public cloud comes with numerous benefits, such as scalability, cost-efficiency, and enhanced collaboration. Due to the current economic circumstances security teams operate under budget constraints. Operational costs. Hence, they are focused on the need to optimize operational spending across two domains.

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