AWS Machine Learning Blog
Category: Customer Solutions
Modernize and migrate on-premises fraud detection machine learning workflows to Amazon SageMaker
Radial is the largest 3PL fulfillment provider, also offering integrated payment, fraud detection, and omnichannel solutions to mid-market and enterprise brands. In this post, we share how Radial optimized the cost and performance of their fraud detection machine learning (ML) applications by modernizing their ML workflow using Amazon SageMaker.
Impel enhances automotive dealership customer experience with fine-tuned LLMs on Amazon SageMaker
In this post, we share how Impel enhances the automotive dealership customer experience with fine-tuned LLMs on SageMaker.
Build a scalable AI assistant to help refugees using AWS
The Danish humanitarian organization Bevar Ukraine has developed a comprehensive virtual generative AI-powered assistant called Victor, aimed at addressing the pressing needs of Ukrainian refugees integrating into Danish society. This post details our technical implementation using AWS services to create a scalable, multilingual AI assistant system that provides automated assistance while maintaining data security and GDPR compliance.
Enhanced diagnostics flow with LLM and Amazon Bedrock agent integration
In this post, we explore how Noodoe uses AI and Amazon Bedrock to optimize EV charging operations. By integrating LLMs, Noodoe enhances station diagnostics, enables dynamic pricing, and delivers multilingual support. These innovations reduce downtime, maximize efficiency, and improve sustainability. Read on to discover how AI is transforming EV charging management.
How ZURU improved the accuracy of floor plan generation by 109% using Amazon Bedrock and Amazon SageMaker
ZURU collaborated with AWS Generative AI Innovation Center and AWS Professional Services to implement a more accurate text-to-floor plan generator using generative AI. In this post, we show you why a solution using a large language model (LLM) was chosen. We explore how model selection, prompt engineering, and fine-tuning can be used to improve results.
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.
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.
GuardianGamer scales family-safe cloud gaming with AWS
In this post, we share how GuardianGamer uses AWS services including Amazon Nova and Amazon Bedrock to deliver a scalable and efficient supervision platform. The team uses Amazon 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.
Principal Financial Group increases Voice Virtual Assistant performance using Genesys, Amazon Lex, and Amazon QuickSight
In this post, we explore how Principal used this opportunity to build an integrated voice VA reporting and analytics solution using an Amazon QuickSight dashboard.
Automating complex document processing: How Onity Group built an intelligent solution using Amazon Bedrock
In this post, we explore how Onity Group, a financial services company specializing in mortgage servicing and origination, transformed their document processing capabilities using Amazon Bedrock and other AWS services. The solution helped Onity achieve a 50% reduction in document extraction costs while improving overall accuracy by 20% compared to their previous OCR and AI/ML solution.