Some 70% of Earth's surface is covered by clouds at any given time, according to NASA. The existence of clouds in optical satellite imagery limits its usefulness, blocking features of interest on the ground. We have developed a machine learning-based approach for detection and segmentation of clouds, and for production of cloud-free mosaics. Our efforts include development of an open dataset consisting of Sentinel-2 imagery and labeled clouds, creation of a lightweight ML architecture for cloud segmentation, and creation of open source tools for inexpensive production of cloud-free mosaics at continent scale. Our dataset and methods are applicable to many types of optical satellite imagery, not just Sentinel-2.