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Tackling our world’s hardest problems with machine learning

Every day, builders are finding new ways to apply machine learning for the benefit of society, from better diagnosis of disease to the protection of endangered species. By putting machine learning technology in the hands of every developer, AWS is committed to enabling our customers to build new, innovative solutions that improve lives and protect our planet.
Explore Customer Stories

Saildrone collects global environment data to monitor the state of the planet in real time using wind-powered ocean drones equipped with climate-grade sensors. Saildrone trains machine learning algorithms to avoid collision with icebergs with cameras mounted on drones – helping to gather new insights into our oceans and climate. 

There are over a billion small-holder farmers who contribute more than 70% of the world’s food. Wefarm works to enable knowledge sharing for these small-holder farmers. With machine learning, Wefarm connects these small-holder farmers to the people and resources they need to achieve their economic potential. 


Understanding Disease Outbreaks

At the beginning of the COVID-19 pandemic, BlueDot, a start-up that uses AI to detect disease outbreaks, was one of the first to raise the alarm about a worrisome outbreak of a respiratory illness in Wuhan, China. Using their machine learning algorithms built on AWS, BlueDot sifts through news reports in 65 languages, along with airline data and animal disease networks to detect outbreaks and anticipate the dispersion of disease. BlueDot provides those insights to public health officials, airlines and hospitals to help them anticipate and better manage risks.

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Empowering the Underbanked

For those in emerging markets, identity verification and validation is one of the major challenges people face to access retail banking services. Aella Credit provides easy access to credit for underbanked consumers in Africa. Aella Credit uses Amazon Rekognition to analyze images to verify a customer’s identity and give them access to financial and healthcare services with minimal friction.

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Finding a Home for Those in Need

PATH’s mission is to end homelessness for individuals, families, and communities. A winner of the AWS Imagine Grant program, PATH is using machine learning to match individuals experiencing homelessness with housing. Amazon Personalize captures relevant information about available housing so case managers can recommend the best possible housing to their clients.

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Working Together to Build Powerful Change

AWS is committed to working with others to encourage the use of machine learning to benefit society, share best practices, accelerate research, and responsibly develop the technology. This collaboration across industry, academia, government and community groups, will help spur innovation for all.


Getting Started with Machine Learning

Across AWS we have many resources that support our customers in getting started with machine learning. Together these resources ensure that our customers have the technical expertise, AWS credits, and education to apply machine learning to their mission.

Machine Learning Solutions Lab

Pairs customers with Amazon machine learning experts to develop solutions that tackle the issues at the heart of the customer’s mission with discovery workshops, ideation sessions and training.

AWS Machine Learning Research Awards

Funds academic research at the forefront of machine learning, providing faculty, PhD candidates, and graduate students with financial support and AWS credits so they can move faster.

AWS Training and Certification

Offers 65+ ML training courses online for free to developers, data scientists, and business decision makers to apply ML to their organizations and unlock new insights.

AWS Imagine Grant Program

Empowers non-profit organizations who are using technology to solve the world’s most pressing problems with financial support, AWS credits, and support from AWS technical specialists.

Public Data Sets Program

Unlocks the potential of open data in the cloud. The program covers the cost of storage for high-value cloud-optimized datasets, accelerating the application of machine learning.