Engineering topics
Bollinger, Drew
Publications and source records attributed to Bollinger, Drew.
End-to-End Machine Learning Applications Framework for Earth Science
No abstract available
Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment
Earth science domain presents unique sets of problems that are increasingly being solved using data driven approaches. The availability of big Earth science data offers immense potential for Machine learning (ML) as evident from numerous research publications lately. However, many of these publications are not ending up as production applications mainly because the data scientists who develop the ML models are now expected to complete the ML lifecycle by deploying and scaling the models in production. We introduce ML lifecycle to the Earth science community including the opportunities and challenges that lie ahead in each phase of the lifecycle. We demonstrate the lifecycle using an Earth science problem that we used ML to address and transitioned to production.
An AI Based System for Objective Tropical Cyclone Intensity Estimation
No abstract available
Deepti: Deep Learning-Based Tropical Cyclone Intensity Estimation
We present the development of a deep learning model for objective estimation of tropical cyclone intensity at a higher temporal frequency, deployment of the model in production, design and implementation of the tropical cyclone monitoring and intensity estimation system and development of an interactive portal for situational awareness and evaluation of intensity estimation.