AWS Machine Learning Blog

In this post, we show how you can automate language localization through translating documents using Amazon Web Services (AWS). The solution combines Amazon Bedrock and AWS Serverless technologies, a suite of fully managed event-driven services for running code, managing data, and integrating applications—all without managing servers.

In this post, we introduce agentic automatic mortgage approval, a next-generation sample solution that uses autonomous AI agents powered by Amazon Bedrock Agents and Amazon Bedrock Data Automation. These agents orchestrate the entire mortgage approval process—intelligently verifying documents, assessing risk, and making data-driven decisions with minimal human intervention.

In this post, we highlight the advanced data augmentation techniques and performance improvements in Amazon Bedrock Model Distillation with Meta’s Llama model family. This technique transfers knowledge from larger, more capable foundation models (FMs) that act as teachers to smaller, more efficient models (students), creating specialized models that excel at specific tasks.

Today, we’re excited to announce that Amazon Q Business now supports anonymous user access. With this new feature, you can now create Amazon Q Business applications with anonymous user mode, where user authentication is not required and content is publicly accessible. In this post, we demonstrate how to build a public-facing generative AI application using Amazon Q Business for anonymous users.

In this post, we share how FloQast built an AI-powered accounting transaction solution using Anthropic’s Claude 3 on Amazon Bedrock.

As generative AI revolutionizes industries, organizations are eager to harness its potential. However, the journey from production-ready solutions to full-scale implementation can present distinct operational and technical considerations. This post explores key insights and lessons learned from AWS customers in Europe, Middle East, and Africa (EMEA) who have successfully navigated this transition, providing a roadmap for others looking to follow suit.

In this post, we explore how AWS services can be seamlessly integrated with open source tools to help establish a robust red teaming mechanism within your organization. Specifically, we discuss Data Reply’s red teaming solution, a comprehensive blueprint to enhance AI safety and responsible AI practices.

This post demonstrates how AWS LLM League’s gamified enablement accelerates partners’ practical AI development capabilities, while showcasing how fine-tuning smaller language models can deliver cost-effective, specialized solutions for specific industry needs.

In this post, we present an LLM migration paradigm and architecture, including a continuous process of model evaluation, prompt generation using Amazon Bedrock, and data-aware optimization. The solution evaluates the model performance before migration and iteratively optimizes the Amazon Nova model prompts using user-provided dataset and objective metrics.

In this post, we demonstrate model customization (fine-tuning) for tool use with Amazon Nova. We first introduce a tool usage use case, and gave details about the dataset. We walk through the details of Amazon Nova specific data formatting and showed how to do tool calling through the Converse and Invoke APIs in Amazon Bedrock. After getting the baseline results from Amazon Nova models, we explain in detail the fine-tuning process, hosting fine-tuned models with provisioned throughput, and using the fine-tuned Amazon Nova models for inference.