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

AWS Machine Learning - AI

Startup probe – Gives the application time to start up. It allows up to 25 minutes for the application to start before considering it failed. These probes assume that your vLLM application exposes a /health endpoint. 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

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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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. AI trained on biased data may produce unreliable results. This customer data, however, remains on customer systems.

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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. These steps are clearly marked in the following diagram.

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

AWS Machine Learning - AI

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies, such as AI21 Labs, Anthropic, Cohere, Meta, Mistral, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

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

AWS Machine Learning - AI

Additionally, SageMaker endpoints support automatic load balancing and autoscaling, enabling your LLM deployment to scale dynamically based on incoming requests. Optimizing these metrics directly enhances user experience, system reliability, and deployment feasibility at scale. deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

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Network topologies – A series: Part 1

Xebia

The first one might even be applicable to home or very small business users. This setup will adopt the usage of cloud load balancing, auto scaling and managed SSL certificates. Virtual machine Because we’re using a load balancer, we can configure a Managed Instance Group to process our traffic.