Category: Advanced (300)

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.

In this post, we’ll walk you through a complete implementation of model fine-tuning in Amazon Bedrock using Amazon Nova models, demonstrating each step through an intent classifier example that achieves superior performance on a domain specific task. Throughout this guide, you’ll learn to prepare high-quality training data that drives meaningful model improvements, configure hyperparameters to optimize learning without overfitting, and deploy your fine-tuned model for improved accuracy and reduced latency. We’ll show you how to evaluate your results using training metrics and loss curves.

In healthcare and life sciences, AI agents help organizations process clinical data, submit regulatory filings, automate medical coding, and accelerate drug development and commercialization. However, the sensitive nature of healthcare data and regulatory requirements like Good Practice (GxP) compliance require human oversight at key decision points. This is where human-in-the-loop (HITL) constructs become essential. In this post, you will learn four practical approaches to implementing human-in-the-loop constructs using AWS services.

This post walks you through understanding audio embeddings, implementing Amazon Nova Multimodal Embeddings, and building a practical search system for your audio content. You’ll learn how embeddings represent audio as vectors, explore the technical capabilities of Amazon Nova, and see hands-on code examples for indexing and querying your audio libraries. By the end, you’ll have the knowledge to deploy production-ready audio search capabilities.

With Amazon Bedrock Projects, you can attribute inference costs to specific workloads and analyze them in AWS Cost Explorer and AWS Data Exports. In this post, you will learn how to set up Projects end-to-end, from designing a tagging strategy to analyzing costs.

This post walks through building an automated podcast generator that creates engaging conversations between two AI hosts on any topic, demonstrating the streaming capabilities of Nova Sonic, stage-aware content filtering, and real-time audio generation.