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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. In this post, we explore a generative AI solution leveraging Amazon Bedrock to streamline the WAFR process.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. For example, consider a text summarization AI assistant intended for academic research and literature review. Such queries could be effectively handled by a simple, lower-cost model.

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Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI

AWS Machine Learning - AI

With the QnABot on AWS (QnABot), integrated with Microsoft Azure Entra ID access controls, Principal launched an intelligent self-service solution rooted in generative AI. This data includes manuals, communications, documents, and other content across various systems like SharePoint, OneNote, and the company’s intranet.

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AI-native software engineering may be closer than developers think

CIO

Developers unimpressed by the early returns of generative AI for coding take note: Software development is headed toward a new era, when most code will be written by AI agents and reviewed by experienced developers, Gartner predicts. Coding agents will need to be transparent and allow programmers to review their output.

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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. To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle.

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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning - AI

Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. You can obtain the SageMaker Unified Studio URL for your domains by accessing the AWS Management Console for Amazon DataZone.

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Create generative AI agents that interact with your companies’ systems in a few clicks using Amazon Bedrock in Amazon SageMaker Unified Studio

AWS Machine Learning - AI

Companies of all sizes face mounting pressure to operate efficiently as they manage growing volumes of data, systems, and customer interactions. Users can access these AI capabilities through their organizations single sign-on (SSO), collaborate with team members, and refine AI applications without needing AWS Management Console access.