The AWS Machine Learning Research Awards (MLRA) assists faculty, PhD candidates, and graduate students with research to advance the frontiers of machine learning (ML) and its application across a wide range of problems---from finding new therapies for cancer to solving climate change and exploring outer space. MLRA provides eligible researchers and university programs cash awards and AWS Promotional Credits so that they can do more faster using the most advanced compute, analytics, and machine learning tools available in the cloud.
Machine learning is still in its evolutionary stage with much of the progress coming from research on innovative algorithms, better data collection and preparation methods, and newer techniques such as reinforcement learning. Until recently, lack of access to the latest compute, storage, and networking has been a blocker for ML research. MLRA solves this problem by offering unrestricted cash awards and access to cutting-edge infrastructure and managed services through AWS Promotional Credits for selected applicants. MLRA also offers award recipients opportunities to participate in AWS events and receive live one-on-one training sessions with AWS data scientists and engineers.
Awards are distributed at the department and project level and are structured as one-time unrestricted gifts to academic institutions.
Credits Awards include AWS Promotional Credits that are redeemed towards eligible AWS Services.
We provide recipients with training resources, including tutorials on how to run machine learning on AWS and hands-on sessions with Amazon scientists and engineers.
Award recipients are invited to a research seminar where they can discuss the progress of their work and interact with other award recipients and Amazon scientists.
Illustration Photo: NASA engineers and scientists have launched a pilot project harnessing the power of machine learning to identify textural patterns unique to life. The idea would be to equip a rover with these sophisticated imaging and data-analysis technologies and allow the instruments to decide in real-time which rocks to sample in the search for life, regardless of how primitive, on the Moon or Mars. (credits: NASA/W. Hrybyk / Flickr Creative Commons Commons Attribution 2.0 Generic (CC BY 2.0))