AI

In today’s world, artificial intelligence chatbots such as ChatGPT and Claude can perform many functions, such as composing work emails and planning travel itineraries. These chatbots are systems built around large vision-language models (VLMs): AI trained on a massive dataset that includes books, websites, code, and images.  The AI algorithms are then refined on massive […]

If you’ve been managing Amazon Quick legacy Topics alongside your datasets, you know the challenge: two assets that must stay perfectly synchronized, each with its own permissions, lineage, and versioning. Column synonyms drift. Calculated fields diverge. A rename in the dataset breaks the Legacy Topic silently. You can now use Amazon Quick to embed that

Business intelligence analysts routinely face the same challenge at the start of every analytics project: the data needed to answer a single business question lives across multiple tables. Sales transactions sit in one place, customer demographics and product attributes in another, while returns, forecasts, and operational metrics occupy still others. Until now, combining these tables

In Part 1 of this series, we introduced Amazon Quick Sight Multi-Dataset Relationships and covered the foundational concepts of dimensional modeling, best practices for designing clean data models, and a decision framework for when to use runtime joins versus pre-joined datasets. If you haven’t read Part 1 yet, we recommend starting there. In this post,

Note: The topics referenced throughout this document refer to the new Topics experience (not legacy Topics). For details on the differences, see Build a unified semantic layer across datasets with multi-dataset Topics in Amazon Quick. Most real-world business questions span multiple tables. A retailer who wants to understand net revenue by product category must draw

Amazon Quick is an AI-powered unified intelligence service that connects structured data and unstructured enterprise content so teams can explore, analyze, and act from one place. Amazon Quick Sight, the business intelligence (BI) capability within Amazon Quick, delivers interactive dashboards, natural language querying, pixel-perfect reports, machine learning (ML)-driven insights, and embedded analytics. Topics in Quick

Building an AI agent that edits images based on natural language requires an orchestration loop, tool routing, memory management, and a compute environment to run it all. Amazon Bedrock AgentCore harness handles that entire stack with configuration. You declare what the agent does, and the harness runs it in a stateful, isolated microVM with built-in

The effectiveness and accuracy of machine learning (ML) models decreases almost as soon as the training job finishes. Changes in consumer behavior, releases of new products, upgrades in sensor technology, and a shifting economic and political landscape are all examples of uncontrollable factors that change the patterns and probabilities the model learned during training. By

Managing AWS infrastructure often means switching between consoles, searching documentation, and manually creating support cases. For each incident, an engineer opens the AWS Management Console, checks Amazon CloudWatch, searches AWS documentation, reviews community posts, and files a support case. This context-switching adds up to 30–45 minutes per investigation before resolution work begins. In this post,

Claude Cowork now keeps working on tasks even after you close your laptop. It’s part of a larger push toward smartphone-controlled agents.