NASA's Projects
Setup an enterprise MediaWiki server with simple commands
Multifidelity Importance Sampling with Python
Tool for investigating and extracting knowledge from large image data sets
MISR INteractive eXplorer
an API facilitating the access of MISR standard product files
Web-based Space Mission Visualization software
The ML-airport-arrival-runway software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting arrival runway assignments. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
The ML-airport-configuration software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting airport configuration as a time series. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
The ML-airport-data-services software is developed to provide common code used throughout the ML-airport suite of software. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides useful functions for development of pipelines including data query and save, data engineering, and data science.
The ML-airport-departure-runway software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting departure runway assignments. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
The ML-airport-estimated-ON software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting arrival ON time. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
The ML-airport-taxi-in software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for four distinct use cases: 1) unimpeded AMA taxi in, 2) unimpeded ramp taxi in, 3) impeded AMA taxi in, and 4) impeded ramp taxi in. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
The ML-airport-taxi-out software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for four distinct use cases: 1) unimpeded AMA taxi out, 2) unimpeded ramp taxi out, 3) impeded AMA taxi out, and 4) impeded ramp taxi out. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
The Core Flight System (cFS) Memory Manager (MM) application.
Marshall MRMS Mosaic Python Toolkit
NASA's Metadata Management Tool.
Shared computing for distributed systems science or autonomy support.
Matplotlib object oriented style system
Contains open-source components of Methane Source Finder backend and front-end. Publishing open-source projects approved through JPL's NTR and open-source policy
Multiple-Doppler Radar Analysis Toolkit (MultiDop)
A front banner for the github.com/nasa page
React
Typescript
Django
Laravel
Facebook
Microsoft
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Alibaba
D3
Tencent