Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment
Manil Maskey, Andrew Molthan, Christopher Hain, Rahul Ramachandran, Iksha Gurung, Brian M. Freitag, J. J. Miller, Muthukumaran Ramasubramanian, Drew Bollinger, Ricardo Mestre, Daniel J. Cecil · 2019
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.