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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 AI products address these two aspects …

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

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 the process of identifying, classifying, prioritizing, …

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

Data is your generative AI differentiator, and successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Traditional data architectures often struggle to meet the unique demands of generative such as applications. An effective generative AI data strategy requires several key components like seamless integration of diverse data sources, …

Build secure RAG applications with AWS serverless data lakes Read More »

NotebookLM is adding featured notebooks, with works selected by partners like The Atlantic and The Economist. ​ 

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 stages, the spectrum of fine-tuning methodologies, …

Advanced fine-tuning methods on Amazon SageMaker AI Read More »

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 remains high. Organizations need infrastructure solutions …

Streamline machine learning workflows with SkyPilot on Amazon SageMaker HyperPod Read More »

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). NER identifies entities from predefined categories, …

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

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, the WWRR organization’s journey toward truly …

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

What if you could replace hours of data analysis with a minute-long conversation? Large language models can transform how we bridge the gap between business questions and actionable data insights. For most organizations, this gap remains stubbornly wide, with business teams trapped in endless cycles—decoding metric definitions and hunting for the correct data sources to …

Build a conversational data assistant, Part 1: Text-to-SQL with Amazon Bedrock Agents Read More »

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 to the Operating model whitepaper for …

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