AWS Machine Learning Blog

This post explores deploying a text-to-SQL pipeline using generative AI models and HAQM 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 HAQM Bedrock Marketplace and HAQM SageMaker JumpStart. In this post, we show you how to get started with Gemma 3 27B Instruct on both HAQM Bedrock Marketplace and SageMaker JumpStart, and how to use the model’s powerful instruction-following capabilities in your applications.

In this post, we walk through building a full-stack application that processes multimodal content using HAQM Bedrock Data Automation, stores the extracted information in an HAQM Bedrock knowledge base, and enables natural language querying through a RAG-based Q&A interface.

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.

HAQM Bedrock Data Automation helps organizations streamline development and boost efficiency through customizable, multimodal analytics. It eliminates the heavy lifting of unstructured content processing at scale, whether for video or audio. The new capabilities make it faster to extract tailored, generative AI-powered insights like scene summaries, key topics, and customer intents from video and audio. This unlocks the value of unstructured content for use cases such as improving sales productivity and enhancing customer experience.

In this post, we share how GuardianGamer uses AWS services including HAQM Nova and HAQM Bedrock to deliver a scalable and efficient supervision platform. The team uses HAQM Nova for intelligent narrative generation to provide parents with meaningful insights into their children’s gaming activities and social interactions, while maintaining a non-intrusive approach to monitoring.

In this post, we explore how Principal used this opportunity to build an integrated voice VA reporting and analytics solution using an HAQM QuickSight dashboard.

This post demonstrates how HAQM Bedrock, combined with a user feedback dataset and few-shot prompting, can refine responses for higher user satisfaction. By using HAQM Titan Text Embeddings v2, we demonstrate a statistically significant improvement in response quality, making it a valuable tool for applications seeking accurate and personalized responses.

HAQM Q Business integration with Microsoft 365 applications offers powerful AI assistance directly within the tools that your team already uses daily. In this post, we explore how these integrations for Outlook and Word can transform your workflow.