Open Source Parallel Image Analysis and Machine Learning Pipeline

Today, NASA researchers must create, debug, and tune custom workflows for each analysis. Creation and modification of custom workflows is fragile, non-portable and consumes time that could be better spent on advancing scientific discovery. The Phase I open source software Ensemble Learning Models (ELM) provides composable, portable, reproducible, and extensible machine learning pipelines with easy-to-configure parallelization, with tools specifically for satellite data processing, weather and climate data processing, and machine learning and prediction. This is a major advancement over the current state-of-the-art because of reduced workflow creation time, parallelization, portability of deployment and use, extensibility, and robustness. Phase II will extend the Phase I work with more options useful to NASA missions, such as advanced ensemble fitting and prediction tools, feature engineering options for 3-D and 4-D arrays, and a web-based map user interface. Phase II will also harden and extend ELM to make ELM's easy-to-use large data ensemble methods accessible to industry outside of NASA, increasing the potential user base in a variety of domains.