Machine Learning Customers
Learn why our customers choose AWS for machine learning
From the world's largest enterprises to emerging start-ups, more machine learning is built on AWS than anywhere else.
AWS customers use machine learning to improve the quality of healthcare, fight human trafficking, provide better customer service, and protect you from fraud. With the broadest and deepest set of machine learning and AI services, they are creating new insights, enabling new efficiencies, and making more accurate predictions. That's why more than 10,000 customers have chosen to use AWS for machine learning.
GE Healthcare uses deep learning on AWS to improve the accuracy of x-ray imaging procedures while also lowering readmission rates.
Zendesk uses deep learning to deliver new capabilities to customer service organizations and scale support for application demand.
Zocdoc uses TensorFlow to create machine learning algorithms that match patients to doctors, reducing the wait time for appointments.
Marinus Analytics uses Amazon Rekognition to help authorities identify human trafficking victims and then prosecute the traffickers.
Curalate uses Apache MXnet to help retailers make products discoverable on social media.
Major League Baseball uses machine learning services on AWS to power Statcast AI.
Rue Gilt Groupe uses Databricks and AWS to create recommendation engines powered by machine learning.
Butterfleye uses Amazon Rekognition to help their cordless security cameras know when to arm or disarm.
Royal FloraHolland works with Xebia to create deep learning applications that drive new efficiencies and improved customer experiences.
The National Football League uses AWS to power Next Gen Stats, which changes the way fans understand and experience football.
DataRobot helps Lenovo Brazil use machine learning on AWS to predict sell-out volume at scale.
HG Data uses machine learning to process raw documents into data.
Crayon helps TINE use machine learning to improve the Norwegian dairy industry.
Sunday uses machine learning to offer their customers a broader range of insurance policies.
Upserve uses machine learning to predict restaurant customer volumes.
Mawdoo3 uses deep learning to enable voice search that works across a range of Arabic dialects.
LinkSquares and SFL Scientific use machine learning to automate post-signature contract reviews.
Policy Bazaar uses Amazon Polly to personalize their response to customer queries.
Thorn collaborates with Amazon Rekognition to help fight child sexual abuse and trafficking.
AdiMap uses machine learning to extrapolate financial ad data for all publishers and advertisers.
Bigfinite uses AI to unlock the value of life sciences manufacturing data.
Haptik uses Amazon Polly to power a virtual personal virtual assistant.
Astro improves email efficiency using a voice assistant based on Amazon Lex.
BuildFax uses Amazon Machine Learning to provide property-specific estimations for insurers and builders.
Fraud.net uses Amazon Machine Learning to continuously identify and protect against new fraud strategies.
zipMoney uses machine learning to help automate underwriting decisions.