Abstract
The many terabytes of data that are generated from space missions must be analyzed, and accurate decisions must be made. This article highlights two important areas where automated machine learning (ML) systems are helping to science the data and allowing engineers and scientists to make more informed decisions without drowning in ones and zeros. One such area is exoplanet detection. This application is particularly unique because data analysis is crowdsourced as part of a citizen science effort in which members of the public who are not part of the science team take part, using powerful open source methods and software tools running on clusters of servers available in the cloud.
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