Build an intelligent eDiscovery solution using Amazon Bedrock Agents

Favorite Legal teams spend bulk of their time manually reviewing documents during eDiscovery. This process involves analyzing electronically stored information across emails, contracts, financial records, and collaboration systems for legal proceedings. This manual approach creates significant bottlenecks: attorneys must identify privileged communications, assess legal risks, extract contractual obligations, and maintain

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Shared by AWS Machine Learning July 25, 2025

Benchmarking Amazon Nova: A comprehensive analysis through MT-Bench and Arena-Hard-Auto

Favorite Large language models (LLMs) have rapidly evolved, becoming integral to applications ranging from conversational AI to complex reasoning tasks. However, as models grow in size and capability, effectively evaluating their performance has become increasingly challenging. Traditional benchmarking metrics like perplexity and BLEU scores often fail to capture the nuances

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Shared by AWS Machine Learning July 24, 2025

Enhance generative AI solutions using Amazon Q index with Model Context Protocol – Part 1

Favorite Today’s enterprises increasingly rely on AI-driven applications to enhance decision-making, streamline workflows, and deliver improved customer experiences. Achieving these outcomes demands secure, timely, and accurate access to authoritative data—especially when such data resides across diverse repositories and applications within strict enterprise security boundaries. Interoperable technologies powered by open standards

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Shared by AWS Machine Learning July 23, 2025

Multi-tenant RAG implementation with Amazon Bedrock and Amazon OpenSearch Service for SaaS using JWT

Favorite In recent years, the emergence of large language models (LLMs) has accelerated AI adoption across various industries. However, to further augment LLMs’ capabilities and effectively use up-to-date information and domain-specific knowledge, integration with external data sources is essential. Retrieval Augmented Generation (RAG) has gained attention as an effective approach

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Shared by AWS Machine Learning July 23, 2025

Beyond accelerators: Lessons from building foundation models on AWS with Japan’s GENIAC program

Favorite In 2024, the Ministry of Economy, Trade and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC)—a Japanese national program to boost generative AI by providing companies with funding, mentorship, and massive compute resources for foundation model (FM) development. AWS was selected as the cloud provider for GENIAC’s second

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Shared by AWS Machine Learning July 22, 2025

Use generative AI in Amazon Bedrock for enhanced recommendation generation in equipment maintenance

Favorite In the manufacturing world, valuable insights from service reports often remain underutilized in document storage systems. This post explores how Amazon Web Services (AWS) customers can build a solution that automates the digitisation and extraction of crucial information from many reports using generative AI. The solution uses Amazon Nova

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Shared by AWS Machine Learning July 21, 2025

Kyruus builds a generative AI provider matching solution on AWS

Favorite This post was written with Zach Heath of Kyruus Health. When health plan members need care, they shouldn’t need a dictionary. Yet millions face this exact challenge—describing symptoms in everyday language while healthcare references clinical terminology and complex specialty classifications. This disconnect forces members to become amateur medical translators,

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Shared by AWS Machine Learning July 21, 2025