Editorial coverage, in-depth analysis, and developer guides — 5 articles.
Online retailers face a persistent challenge: shoppers struggle to determine the fit and look when ordering online, leading to increased returns and decreased purchase confidence. The cost? Lost revenue, operational overhead, and customer frustration. Meanwhile, consumers increasingly expect immersive, interactive shopping experiences that bridge the gap between online and in-store retail. Retailers implementing virtual try-on […]
In this post, you will learn how speculative decoding works and why it helps reduce cost per generated token on AWS Trainium2.
In this post, we introduce the Generative AI Path-to-Value (P2V) framework, a structured approach to help you move generative AI initiatives from concept to production and sustained value creation.
This post demonstrates how Lambda enables scalable, cost-effective reward functions for Amazon Nova customization. You'll learn to choose between Reinforcement Learning via Verifiable Rewards (RLVR) for objectively verifiable tasks and Reinforcement Learning via AI Feedback (RLAIF) for subjective evaluation, design multi-dimensional reward systems that help you prevent reward hacking, optimize Lambda functions for training scale, and monitor reward distributions with Amazon CloudWatch. Working code examples and deployment guidance are included to help you start experimenting.
This post walks you through three steps: starting a session and generating the Live View URL, rendering the stream in your React application, and wiring up an AI agent that drives the browser while your users watch. At the end, you will have a working sample application you can clone and run.