AWS Machine Learning Blog
Tag: AWS Customer
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.
Fast-track SOP processing using Amazon Bedrock
When a regulatory body like the US Food and Drug Administration (FDA) introduces changes to regulations, organizations are required to evaluate the changes against their internal SOPs. When necessary, they must update their SOPs to align with the regulation changes and maintain compliance. In this post, we show different approaches using Amazon Bedrock to identify relationships between regulation changes and SOPs.
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.
How Planview built a scalable AI Assistant for portfolio and project management using Amazon Bedrock
In this post, we explore how Planview was able to develop a generative AI assistant to address complex work management process by adopting Amazon Bedrock.
How DPG Media uses Amazon Bedrock and Amazon Transcribe to enhance video metadata with AI-powered pipelines
In this post, we share how DPG Media is introducing AI-powered processes using Amazon Bedrock into its video publication pipelines. This solution is helping accelerate audio metadata extraction, create a more engaging user experience, and save time.
How Twilio generated SQL using Looker Modeling Language data with Amazon Bedrock
As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. This post highlights how Twilio enabled natural language-driven data exploration of business intelligence (BI) data with RAG and Amazon Bedrock.
Monks boosts processing speed by four times for real-time diffusion AI image generation using Amazon SageMaker and AWS Inferentia2
This post is co-written with Benjamin Moody from Monks. Monks is the global, purely digital, unitary operating brand of S4Capital plc. With a legacy of innovation and specialized expertise, Monks combines an extraordinary range of global marketing and technology services to accelerate business possibilities and redefine how brands and businesses interact with the world. Its […]
Real estate brokerage firm John L. Scott uses Amazon Textract and Amazon Comprehend to strike racially restrictive language from property deeds for homeowners
Founded more than 91 years ago in Seattle, John L. Scott Real Estate’s core value is Living Life as a Contribution®. The firm helps homebuyers find and buy the home of their dreams, while also helping sellers move into the next chapter of their home ownership journey. John L. Scott currently operates over 100 offices […]
Enhance sports narratives with natural language generation using Amazon SageMaker
This blog post was co-authored by Arbi Tamrazian, Director of Data Science and Machine Learning at Fox Sports. FOX Sports is the sports television arm of FOX Network. The company used machine learning (ML) and Amazon SageMaker to streamline the production of relevant in-game storylines for commentators to use during live broadcasts. “We collaborated with […]
Accelerating MLOps at Bayer Crop Science with Kubeflow Pipelines and Amazon SageMaker
This is a guest post by the data science team at Bayer Crop Science. Farmers have always collected and evaluated a large amount of data with each growing season: seeds planted, crop protection inputs applied, crops harvested, and much more. The rise of data science and digital technologies provides farmers with a wealth of new […]