Category: Technical How-to

In this post, we explore how Amazon Nova Canvas can solve real-world business challenges through advanced image generation techniques. We focus on two specific use cases that demonstrate the power and flexibility of this technology: interior design and product photography.

This post explores deploying a text-to-SQL pipeline using generative AI models and Amazon Bedrock to ask natural language questions to a genomics database. We demonstrate how to implement an AI assistant web interface with AWS Amplify and explain the prompt engineering strategies adopted to generate the SQL queries. Finally, we present instructions to deploy the service in your own AWS account.

We are excited to announce the availability of Gemma 3 27B Instruct models through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. In this post, we show you how to get started with Gemma 3 27B Instruct on both Amazon Bedrock Marketplace and SageMaker JumpStart, and how to use the model’s powerful instruction-following capabilities in your applications.

In this post, we show you how to implement and evaluate three powerful techniques for tailoring FMs to your business needs: RAG, fine-tuning, and a hybrid approach combining both methods. We provid ready-to-use code to help you experiment with these approaches and make informed decisions based on your specific use case and dataset.

Rufus, an AI-powered shopping assistant, relies on many components to deliver its customer experience including a foundation LLM (for response generation) and a query planner (QP) model for query classification and retrieval enhancement. This post focuses on how the QP model used draft centric speculative decoding (SD)—also called parallel decoding—with AWS AI chips to meet the demands of Prime Day. By combining parallel decoding with AWS Trainium and Inferentia chips, Rufus achieved two times faster response times, a 50% reduction in inference costs, and seamless scalability during peak traffic.

In this post, we present a solution to incorporate Amazon Bedrock Agents in your Slack workspace. We guide you through configuring a Slack workspace, deploying integration components in Amazon Web Services, and using this solution.

In this post, we introduce a multi-agent collaboration pipeline for processing unstructured insurance data using Amazon Bedrock, featuring specialized agents for classification, conversion, and metadata extraction. We demonstrate how this domain-aware approach transforms diverse data formats like claims documents, videos, and audio files into metadata-rich outputs that enable fraud detection, customer 360-degree views, and advanced analytics.

In this post, we demonstrate how to build a custom plugin with Amazon Q Business for backend integration. This plugin can integrate existing systems, including third-party systems, with little to no development in just weeks and automate critical workflows. Additionally, we show how to safeguard the solution using Amazon Cognito and AWS IAM Identity Center, maintaining the safety and integrity of sensitive data and workflows.

In this post, we demonstrate how to retrain and quantize a model using SageMaker AI and the SiMa.ai Palette software suite. The goal is to accurately detect individuals in environments where visibility and protective equipment detection are essential for compliance and safety.

This post is the first in a series where we will run multiple diffusion transformers on Trainium and Inferentia-powered instances. In this post, we show how you can deploy PixArt-Sigma to Trainium and Inferentia-powered instances.