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Can we trust Google Cloud Load Balancing?

Xebia

With Cloud getting a more prominent place in the digital world and with that Cloud Service Providers (CSP), it triggered the question on how secure our data with Google Cloud actually is when looking at their Cloud Load Balancing offering. During threat modelling, the SSL Load Balancing offerings often come into the picture.

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

AWS Machine Learning - AI

AWS Trainium and AWS Inferentia based instances, combined with Amazon Elastic Kubernetes Service (Amazon EKS), provide a performant and low cost framework to run LLMs efficiently in a containerized environment. We also demonstrate how to test the solution and monitor performance, and discuss options for scaling and multi-tenancy.

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

AWS Machine Learning - AI

Shared components refer to the functionality and features shared by all tenants. Load balancer – Another option is to use a load balancer that exposes an HTTPS endpoint and routes the request to the orchestrator. You can use AWS services such as Application Load Balancer to implement this approach.

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

AWS Machine Learning - AI

If you don’t have an AWS account, refer to How do I create and activate a new Amazon Web Services account? If you don’t have an existing knowledge base, refer to Create an Amazon Bedrock knowledge base. Performance optimization The serverless architecture used in this post provides a scalable solution out of the box.

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Seeing through hardware counters: a journey to threefold performance increase

Netflix Tech

By Vadim Filanovsky and Harshad Sane In one of our previous blogposts, A Microscope on Microservices we outlined three broad domains of observability (or “levels of magnification,” as we referred to them)?—?Fleet-wide, Luckily, the m5.12xl instance type exposes a set of core PMCs (Performance Monitoring Counters, a.k.a.

Hardware 145
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Optimize hosting DeepSeek-R1 distilled models with Hugging Face TGI on Amazon SageMaker AI

AWS Machine Learning - AI

The following figure illustrates the performance of DeepSeek-R1 compared to other state-of-the-art models on standard benchmark tests, such as MATH-500 , MMLU , and more. To learn more about Hugging Face TGI support on Amazon SageMaker AI, refer to this announcement post and this documentation on deploy models to Amazon SageMaker AI.

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Security Reference Architecture Summary for Cloudera Data Platform

Cloudera

The cluster architecture can be split across a number of zones as illustrated in the following diagram: Outside the perimeter are source data and applications, the gateway zones are where administrators and applications will interact with the core cluster zones where the work is performed. All policies are maintained by the Ranger service.