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Rad AI reduces real-time inference latency by 50% using Amazon SageMaker

AWS Machine Learning Blog

Let’s transition to exploring solutions and architectural strategies. Approaches to researcher productivity To translate our strategic planning into action, we developed approaches focused on refining our processes and system architectures. He has a passion for continuous innovation and using data to drive business outcomes.

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Build a dynamic, role-based AI agent using Amazon Bedrock inline agents

AWS Machine Learning Blog

AWS Lambda functions for executing specific actions (such as submitting vacation requests or expense reports). To understand how this dynamic role-based functionality works under the hood, lets examine the following system architecture diagram. Maira Ladeira Tanke is a Senior Generative AI Data Scientist at AWS.

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Accelerate pre-training of Mistral’s Mathstral model with highly resilient clusters on Amazon SageMaker HyperPod

AWS Machine Learning Blog

The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud. at a minimum).

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Accelerate disaster response with computer vision for satellite imagery using Amazon SageMaker and Amazon Augmented AI

AWS Machine Learning Blog

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. It enables us to iteratively add context to the image, without having to recompile or rescale, and handle schema evolution.

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LLMOps: What It Is, Why It Matters, and How to Implement It

The MLOps Blog

Observability tools: Use platforms that offer comprehensive observability into LLM performance, including functional logs (prompt-completion pairs) and operational metrics (system health, usage statistics). Caption : RAG system architecture.

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Mitigating risk: AWS backbone network traffic prediction using GraphStorm

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The AWS global backbone network is the critical foundation enabling reliable and secure service delivery across AWS Regions. Specifically, we need to predict how changes to one part of the AWS global backbone network might affect traffic patterns and performance across the entire system.

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Innovating at speed: BMW’s generative AI solution for cloud incident analysis

AWS Machine Learning Blog

In this post, we explain how BMW uses generative AI technology on AWS to help run these digital services with high availability. Moreover, these teams might be geographically dispersed and run their workloads in different locations and regions; many hosted on AWS, some elsewhere.

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