Build a conversational data assistant, Part 2 – Embedding generative business intelligence with Amazon Q in QuickSight

Favorite In Part 1 of this series, we explored how Amazon’s Worldwide Returns & ReCommerce (WWRR) organization built the Returns & ReCommerce Data Assist (RRDA)—a generative AI solution that transforms natural language questions into validated SQL queries using Amazon Bedrock Agents. Although this capability improves data access for technical users,

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Shared by AWS Machine Learning July 11, 2025

Intelligent document processing at scale with generative AI and Amazon Bedrock Data Automation

Favorite Extracting information from unstructured documents at scale is a recurring business task. Common use cases include creating product feature tables from descriptions, extracting metadata from documents, and analyzing legal contracts, customer reviews, news articles, and more. A classic approach to extracting information from text is named entity recognition (NER).

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Shared by AWS Machine Learning July 11, 2025

Streamline machine learning workflows with SkyPilot on Amazon SageMaker HyperPod

Favorite This post is co-written with Zhanghao Wu, co-creator of SkyPilot. The rapid advancement of generative AI and foundation models (FMs) has significantly increased computational resource requirements for machine learning (ML) workloads. Modern ML pipelines require efficient systems for distributing workloads across accelerated compute resources, while making sure developer productivity

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Shared by AWS Machine Learning July 11, 2025

Advanced fine-tuning methods on Amazon SageMaker AI

Favorite This post provides the theoretical foundation and practical insights needed to navigate the complexities of LLM development on Amazon SageMaker AI, helping organizations make optimal choices for their specific use cases, resource constraints, and business objectives. We also address the three fundamental aspects of LLM development: the core lifecycle

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Shared by AWS Machine Learning July 11, 2025

Unlock retail intelligence by transforming data into actionable insights using generative AI with Amazon Q Business

Favorite Businesses often face challenges in managing and deriving value from their data. According to McKinsey, 78% of organizations now use AI in at least one business function (as of 2024), showing the growing importance of AI solutions in business. Additionally, 21% of organizations using generative AI have fundamentally redesigned

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Shared by AWS Machine Learning July 10, 2025

AWS AI infrastructure with NVIDIA Blackwell: Two powerful compute solutions for the next frontier of AI

Favorite Imagine a system that can explore multiple approaches to complex problems, drawing on its understanding of vast amounts of data, from scientific datasets to source code to business documents, and reasoning through the possibilities in real time. This lightning-fast reasoning isn’t waiting on the horizon. It’s happening today in

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Shared by AWS Machine Learning July 10, 2025

Build real-time conversational AI experiences using Amazon Nova Sonic and LiveKit

Favorite The rapid growth of generative AI technology has been a catalyst for business productivity growth, creating new opportunities for greater efficiency, enhanced customer service experiences, and more successful customer outcomes. Today’s generative AI advances are helping existing technologies achieve their long-promised potential. For example, voice-first applications have been gaining

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Shared by AWS Machine Learning July 10, 2025

Build an MCP application with Mistral models on AWS

Favorite This post is cowritten with Siddhant Waghjale and Samuel Barry from Mistral AI. Model Context Protocol (MCP) is a standard that has been gaining significant traction in recent months. At a high level, it consists of a standardized interface designed to streamline and enhance how AI models interact with

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Shared by AWS Machine Learning July 10, 2025

Use K8sGPT and Amazon Bedrock for simplified Kubernetes cluster maintenance

Favorite As Kubernetes clusters grow in complexity, managing them efficiently becomes increasingly challenging. Troubleshooting modern Kubernetes environments requires deep expertise across multiple domains—networking, storage, security, and the expanding ecosystem of CNCF plugins. With Kubernetes now hosting mission-critical workloads, rapid issue resolution has become paramount to maintaining business continuity. Integrating advanced

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Shared by AWS Machine Learning July 10, 2025