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Amazon operations span the globe, touching the lives of millions of customers, employees, and vendors every day. From the vast logistics network to the cutting-edge technology infrastructure, this scale is a testament to the company’s ability to innovate and serve its customers. With this scale comes a responsibility to manage risks and address claims—whether they […]
We’re excited to announce that Amazon Bedrock Custom Model Import now supports Qwen models. You can now import custom weights for Qwen2, Qwen2_VL, and Qwen2_5_VL architectures, including models like Qwen 2, 2.5 Coder, Qwen 2.5 VL, and QwQ 32B. You can bring your own customized Qwen models into Amazon Bedrock and deploy them in a fully managed, serverless environment—without having to
Deploy Qwen models with Amazon Bedrock Custom Model Import Read More »
This post was co-written with Renato Nascimento, Felipe Viana, Andre Von Zuben from Articul8. Generative AI is reshaping industries, offering new efficiencies, automation, and innovation. However, generative AI requires powerful, scalable, and resilient infrastructures that optimize large-scale model training, providing rapid iteration and efficient compute utilization with purpose-built infrastructure and automated cluster management. In this
This post was co-written with Suzanne Willard and Makoto Uchida from VideoAmp. In this post, we illustrate how VideoAmp, a media measurement company, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a prototype of the VideoAmp Natural Language (NL) Analytics Chatbot to uncover meaningful insights at scale within media analytics data
How VideoAmp uses Amazon Bedrock to power their media analytics interface Read More »
For an AI model to perform effectively in specialized domains, it requires access to relevant background knowledge. A customer support chat assistant, for instance, needs detailed information about the business it serves, and a legal analysis tool must draw upon a comprehensive database of past cases. To equip large language models (LLMs) with this knowledge,
Contextual retrieval in Anthropic using Amazon Bedrock Knowledge Bases Read More »
Prompt caching in Amazon Bedrock is now generally available, delivering performance and cost benefits for agentic AI applications. Coding assistants that process large codebases represent an ideal use case for prompt caching. In this post, we’ll explore how to combine Amazon Bedrock prompt caching with Claude Code—a coding agent released by Anthropic that is now
Supercharge your development with Claude Code and Amazon Bedrock prompt caching Read More »
We’ve witnessed remarkable advances in model capabilities as generative AI companies have invested in developing their offerings. Language models such as Anthropic’s Claude Opus 4 & Sonnet 4, Amazon Nova, and Amazon Bedrock can reason, write, and generate responses with increasing sophistication. But even as these models grow more powerful, they can only work with
Unlocking the power of Model Context Protocol (MCP) on AWS Read More »
Standard operating procedures (SOPs) are essential documents in the context of regulations and compliance. SOPs outline specific steps for various processes, making sure practices are consistent, efficient, and compliant with regulatory standards. SOP documents typically include key sections such as the title, scope, purpose, responsibilities, procedures, documentation, citations (references), and a detailed approval and revision
Fast-track SOP processing using Amazon Bedrock Read More »
Emerging transformer-based vision models for geospatial data—also called geospatial foundation models (GeoFMs)—offer a new and powerful technology for mapping the earth’s surface at a continental scale, providing stakeholders with the tooling to detect and monitor surface-level ecosystem conditions such as forest degradation, natural disaster impact, crop yield, and many others. GeoFMs represent an emerging research
Revolutionizing earth observation with geospatial foundation models on AWS Read More »
Foundation models (FMs) have revolutionised AI capabilities, but adopting them for specific business needs can be challenging. Organizations often struggle with balancing model performance, cost-efficiency, and the need for domain-specific knowledge. This blog post explores three powerful techniques for tailoring FMs to your unique requirements: Retrieval Augmented Generation (RAG), fine-tuning, and a hybrid approach combining
