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1. Revolutionizing earth observation with geospatial foundation models on AWS

   In this post, we explore how a leading GeoFM (Clay Foundation’s Clay foundation model available on Hugging Face) can be deployed for large-scale inference and fine-tuning on Amazon SageMaker.

2. Create an agentic RAG application for advanced knowledge discovery with LlamaIndex, and Mistral in Amazon Bedrock

   In this post, we demonstrate an example of building an agentic RAG application using the LlamaIndex framework. LlamaIndex is a framework that connects FMs with external data sources. It helps ingest, structure, and retrieve information from databases, APIs, PDFs, and more, enabling the agent and RAG for AI applications. This application serves as a research tool, using the Mistral Large 2 FM on...

3. Text-to-image basics with Amazon Nova Canvas

   In this post, we focus on the Amazon Nova Canvas image generation model. We then provide an overview of the image generation process (diffusion) and dive deep into the input parameters for text-to-image generation with Amazon Nova Canvas.

4. Real-world applications of Amazon Nova Canvas for interior design and product photography

   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.

5. Part 3: Building an AI-powered assistant for investment research with multi-agent collaboration in Amazon Bedrock and Amazon Bedrock Data Automation

   In this post, we walk through how to build a multi-agent investment research assistant using the multi-agent collaboration capability of Amazon Bedrock. Our solution demonstrates how a team of specialized AI agents can work together to analyze financial news, evaluate stock performance, optimize portfolio allocations, and deliver comprehensive investment insights—all orchestrated through a...

6. A generative AI prototype with Amazon Bedrock transforms life sciences and the genome analysis process

   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.

7. Gemma 3 27B model now available on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart

   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.

8. Building a multimodal RAG based application using Amazon Bedrock Data Automation and Amazon Bedrock Knowledge Bases

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

9. Tailoring foundation models for your business needs: A comprehensive guide to RAG, fine-tuning, and hybrid approaches

   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.

10. How Rufus doubled their inference speed and handled Prime Day traffic with AWS AI chips and parallel decoding

    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...