AWS 部落格

Amazon SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with Amazon S3 Tables, the first cloud object store with built-in Apache Iceberg support. In this post, we guide you how to use various analytics services using the integration of SageMaker Lakehouse with S3 Tables.

Amazon Web Services (AWS) is pleased to announce the release of new Security Reference Architecture (SRA) code examples for securing generative AI workloads. The examples include two comprehensive capabilities focusing on secure model inference and RAG implementations, covering a wide range of security best practices using AWS generative AI services. These new code examples are [...]

This post demonstrates how Zoom users can access their Amazon Q Business enterprise data directly within their Zoom interface, alleviating the need to switch between applications while maintaining enterprise security boundaries. Organizations can now configure Zoom as a data accessor in Amazon Q Business, enabling seamless integration between their Amazon Q index and Zoom AI Companion. This integration allows users to access their enterprise knowledge in a controlled manner directly within the Zoom platform.

Note: This post was first published April 21, 2016. The updated version aligns with the latest version of AWS WAF (AWS WAF v2) and includes screenshots that reflect the changes in the AWS console experience. AWS WAF Classic has been deprecated and will be end-of-life (EOL) in September 2025. This update describes how to use [...]

In this post, we explore how QyrusAI and Amazon Bedrock are revolutionizing shift-left testing, enabling teams to deliver better software faster. Amazon Bedrock is a fully managed service that allows businesses to build and scale generative AI applications using foundation models (FMs) from leading AI providers. It enables seamless integration with AWS services, offering customization, security, and scalability without managing infrastructure.

Amazon Bedrock Data Automation (BDA) is a new managed feature powered by FMs in Amazon Bedrock. BDA extracts structured outputs from unstructured content—including documents, images, video, and audio—while alleviating the need for complex custom workflows. In this post, we demonstrate how BDA automatically extracts rich video insights such as chapter segments and audio segments, detects text in scenes, and classifies Interactive Advertising Bureau (IAB) taxonomies, and then uses these insights to build a nonlinear ads solution to enhance contextual advertising effectiveness.

This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. The Salesforce AI Model Serving team is working to push the boundaries of natural language processing and AI capabilities for enterprise applications. Their key focus areas include optimizing large [...]

In the rapidly evolving landscape of Generative AI (GenAI), Orkes Conductor emerges as a powerful orchestration platform that helps organizations manage complex workflows, integrate with AWS services, and implement essential safeguards for GenAI applications. The platform offers pre-built LLM tasks, robust error handling, and monitoring capabilities, enabling developers to create sophisticated AI workflows while providing enterprise features like RBAC, audit logs, and versioning for supported applications.

In this post, we discuss how the University of Johannesburg worked with CX/SIM, an AWS Partner, to develop an AI-powered service automation chatbot that increased student engagement and reduced costs.

Tiny microscopic differences that we could never dream of seeing with our eyes can make the difference between a cutting edge therapeutic and a lethal poison. In the not too distant past it would have been impossible to imagine the treatments we have access to today and the broad range of conditions we can target [...]