nasa/ML-airport-configuration
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.
PythonNOASSERTION
Stargazers
- andrewn6@instafleet
- Asierzubia
- astrojuanlu@kedro-org
- AzuremisAdeptus Mechanicus
- bash-jAdelaide, South Australia
- brnaguiar@detiuaveiro
- brycetford
- chrisschoppOntario
- cpinon-grdGradiant
- culverit
- datajoelyQuantumBlack
- DavidRamosSalUniversidad Industrial de Santander
- dclambert
- DebanjanBanerjeeQBMcKinsey and Company
- dryuvrajk
- ejwillemseSecond Order AI
- emilnygaardfriskCopenhagen, Denmark
- jolbyEvocomputing, Inc.
- kprybolThe Wound Pros
- lucasmelin@hashicorp
- nicolasboisseau
- RK22000Bay Area | San Jose
- spex66Cognite ASA / Dr. Krusche & Partner PartG
- ThunderBuilder
- yetudada@kedro-org
- zawarudo