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Deploy DeepSeek-R1 Distilled Llama models in Amazon Bedrock

AWS Machine Learning - AI

DeepSeek-R1 distilled variations From the foundation of DeepSeek-R1, DeepSeek AI has created a series of distilled models based on both Metas Llama and Qwen architectures, ranging from 1.570 billion parameters. Sufficient local storage space, at least 17 GB for the 8B model or 135 GB for the 70B model.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. The solution incorporates the following key features: Using a Retrieval Augmented Generation (RAG) architecture, the system generates a context-aware detailed assessment.

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The future of Cloud-native software development with Radius

Xebia

Initially, our industry relied on monolithic architectures, where the entire application was a single, simple, cohesive unit. Ever increasing complexity To overcome these limitations, we transitioned to Service-Oriented Architecture (SOA). The first demo app does not have a database yet, but we will add it later.)

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Using Amazon Q Business with AWS HealthScribe to gain insights from patient consultations

AWS Machine Learning - AI

It doesn’t retain audio or output text, and users have control over data storage with encryption in transit and at rest. Architecture diagram In the architecture diagram we present for this demo, two user workflows are shown. For more details, see Configure user access with the default IAM Identity Center directory.

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5 You’re Probably F**king Up Your Microservices

OverOps

It seems like everyone is into microservices these days, and monolith architectures are slowly fading into obscurity. In monolithic applications, it is reflected in the separation of Presentation, Business and Data Layers in a typical 3-tier architecture. Editor’s Note: This post was originally published on May 5, 2016. Final Thoughts.

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

AWS Machine Learning - AI

By implementing this architectural pattern, organizations that use Google Workspace can empower their workforce to access groundbreaking AI solutions powered by Amazon Web Services (AWS) and make informed decisions without leaving their collaboration tool. In the following sections, we explain how to deploy this architecture.

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Boosting team innovation, productivity, and knowledge sharing with Amazon Q Business – Web experience

AWS Machine Learning - AI

The following diagram shows the reference architecture for various personas, including developers, support engineers, DevOps, and FinOps to connect with internal databases and the web using Amazon Q Business. The following demos are examples of what the Amazon Q Business web experience looks like.