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Building Resilient Public Networking on AWS: Part 4

Xebia

One of the key differences between the approach in this post and the previous one is that here, the Application Load Balancers (ALBs) are private, so the only element exposed directly to the Internet is the Global Accelerator and its Edge locations. In the following sections we will review this step-by-step region evacuation example.

AWS 130
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Composite AI: The trifecta that is transforming AIOps

CIO

For example, if a company’s e-commerce website is taking too long to process customer transactions, a causal AI model determines the root cause (or causes) of the delay, such as a misconfigured load balancer. First, a brief description of these three types of AI: Causal AI analyzes data to infer the root causes of events.

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

AWS Machine Learning - AI

The AWS Command Line Interface (AWS CLI) installed eksctl kubectl docker In this post, the examples use an inf2.48xlarge instance; make sure you have a sufficient service quota to use this instance. As a result, traffic won’t be balanced across all replicas of your deployment.

AWS 88
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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

It contains services used to onboard, manage, and operate the environment, for example, to onboard and off-board tenants, users, and models, assign quotas to different tenants, and authentication and authorization microservices. You can use AWS services such as Application Load Balancer to implement this approach.

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Network topologies – A series: Part 1

Xebia

The examples will be presented as Google Cloud Platform (GCP) resources, but can in most cases be inferred to other public cloud vendors. This setup will adopt the usage of cloud load balancing, auto scaling and managed SSL certificates. Network This example will use the same network as from the previous example.

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

AWS Machine Learning - AI

For example, MaestroQA offers sentiment analysis for customers to identify the sentiment of their end customer during the support interaction, enabling MaestroQAs customers to sort their interactions and manually inspect the best or worst interactions. For example, Can I speak to your manager?

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

AWS Machine Learning - AI

For example, DeepSeek-V3 is a 671-billion-parameter model, but only 37 billion parameters (approximately 5%) are activated during the output of each token. Additionally, SageMaker endpoints support automatic load balancing and autoscaling, enabling your LLM deployment to scale dynamically based on incoming requests.