Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment
- Resource Type
- Conference
- Authors
- Maskey, Manil; Ramachandran, Rahul; Gurung, Iksha; Freitag, Brian; Miller, J. J.; Ramasubramanian, Muthukumaran; Bollinger, Drew; Mestre, Ricardo; Cecil, Daniel; Molthan, Andrew; Hain, Chris
- Source
- IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2019 - 2019 IEEE International. :10043-10046 Jul, 2019
- Subject
- Aerospace
Geoscience
Signal Processing and Analysis
Production
Data models
Tropical cyclones
Estimation
Wind speed
Machine learning
neural network
Earth science
production model
life cycle
labeled data
training
- Language
- ISSN
- 2153-7003
Enterprises are making machine learning for production as an integral part of their future roadmaps and Earth science domain is no exception. However, there is common problem in transitioning machine learning from science to production due to a major difference in constructing a model versus deploying it for people to use to make decisions. Phases of machine learning lifecycle that includes model transition to production using a successful application is discussed.