FOR THIS VISION FOR DAAS TO SUCCEED WE MUST INVEST IN SUPPORTING THE FOLLOWING TECHNOLOGY AREAS ML WORKFLOW…
Award details are derived from public USAspending.gov records. Not a bid recommendation.
Award details
- Award ID
- 80NSSC19C0237
- Contractor
- Awarding agency
- National Aeronautics and Space Administration
- Sub-agency / office
- National Aeronautics and Space Administration
- Award amount
- $100,000
- Period of performance
- Jun 26, 2019 – Jun 25, 2020
- NAICS
- 541715 — Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
- PSC
- PSC AR11
- Place of performance
- MD
Public award description
FOR THIS VISION FOR DAAS TO SUCCEED WE MUST INVEST IN SUPPORTING THE FOLLOWING TECHNOLOGY AREAS ML WORKFLOW MANAGEMENT: AS ANY ML RESEARCHER KNOWS, TRAINING A MODEL IS ONLY A SMALL PART OF THE ENTIRE PROCESS. THIS PROCESS BEGINS WITH DATA MANAGEMENT, MODEL TRAINING, MODEL TESTING, DEPLOYMENT, AGGREGATION OF RESULTS, AND UPDATING THE MODELS. ML MODEL DEVELOPMENT LIFECYCLE: THE PROCESS OF DEVELOPING A MODEL IS ITERATIVE, INVOLVING SEVERAL LOOPS OF TRAINING AND TESTING. IT IS IMPORTANT TO ENSURE THESE LOOPS ARE PROCESSED QUICKLY AND ARE TRACTABLE. MODULARITY IN OPERATIONS: EACH MODEL IS DIFFERENT AND CAN RELY ON SPECIALIZED DATA, LIBRARIES ETC. THE PLATFORM SHOULD ALSO BE ABLE TO ADDRESS THESE REQUIREMENTS ALONG WITH THOSE USED BY MAJORITY OF USERS. MODULARIZED PROCESSES WILL HELP IN RESEARCHERS PICK AND CHOOSE THE EXACT COMBINATION OF DATA, AND TOOLS THAT WORKS FOR THEM.
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