7 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. Other Administrative Action $0 11/7/19 6 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. Other Administrative Action $0 7/23/19 4 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. Other Administrative Action $0 6/7/18 5 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. Exercise an Option $374.9k 6/7/18 3 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. Other Administrative Action $0 6/7/18