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.