Tackling our world’s hardest problems with machine learning
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.
Commercial buildings are responsible for 40% of U.S. emissions. Learn how Carbon Lighthouse uses machine learning on AWS to develop insights that deliver energy savings and decrease CO2 emissions in commercial real estate, reducing over 260,000 metric tons of emissions to date.
Faster intervention of military community suicides
Experts at the Amazon Machine Learning Solutions Lab worked with RallyPoint military social media platform and Harvard University's Nock Lab to use machine learning built on Amazon SageMaker to aid suicide intervention efforts by analyzing at-risk public posts.
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.
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.
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.
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.
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.
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.