How Rapid7 automates vulnerability risk scores with ML pipelines using Amazon SageMaker AI

Favorite This post is cowritten with Jimmy Cancilla from Rapid7. Organizations are managing increasingly distributed systems, which span on-premises infrastructure, cloud services, and edge devices. As systems become interconnected and exchange data, the potential pathways for exploitation multiply, and vulnerability management becomes critical to managing risk. Vulnerability management (VM) is

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Shared by AWS Machine Learning July 14, 2025

Build AI-driven policy creation for vehicle data collection and automation using Amazon Bedrock

Favorite Vehicle data is critical for original equipment manufacturers (OEMs) to drive continuous product innovation and performance improvements and to support new value-added services. Similarly, the increasing digitalization of vehicle architectures and adoption of software-configurable functions allow OEMs to add new features and capabilities efficiently. Sonatus’s Collector AI and Automator

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Shared by AWS Machine Learning July 14, 2025

Fraud detection empowered by federated learning with the Flower framework on Amazon SageMaker AI

Favorite Fraud detection remains a significant challenge in the financial industry, requiring advanced machine learning (ML) techniques to detect fraudulent patterns while maintaining compliance with strict privacy regulations. Traditional ML models often rely on centralized data aggregation, which raises concerns about data security and regulatory constraints. Fraud cost businesses over

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Shared by AWS Machine Learning July 11, 2025

Long-running execution flows now supported in Amazon Bedrock Flows in public preview

Favorite Today, we announce the public preview of long-running execution (asynchronous) flow support within Amazon Bedrock Flows. With Amazon Bedrock Flows, you can link foundation models (FMs), Amazon Bedrock Prompt Management, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, and other AWS services together to build and scale

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Shared by AWS Machine Learning July 11, 2025

Implement user-level access control for multi-tenant ML platforms on Amazon SageMaker AI

Favorite Managing access control in enterprise machine learning (ML) environments presents significant challenges, particularly when multiple teams share Amazon SageMaker AI resources within a single Amazon Web Services (AWS) account. Although Amazon SageMaker Studio provides user-level execution roles, this approach becomes unwieldy as organizations scale and team sizes grow. Refer

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Shared by AWS Machine Learning July 11, 2025

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