Machine Learning Summer School
In collaboration with the IEEE GRSS Earth Science Informatics Technical Committee, IMPACT held a machine learning summer school session for twenty-six global and diverse students. The session on “Scaling Machine Learning for Remote Sensing on Cloud Computing Environment” took place during the 4-day IEEE-GRSS event hosted by the working group on High-performance and Disruptive Computing in Remote Sensing (HDCRS).
The goals of the session were multifaceted and included a focus on:
- providing technical guidance on performing an end-to-end machine learning use case for remote sensing;
- utilizing cloud computing for machine learning on remote sensing;
- promoting open science via collaboration;
- developing machine learning expertise for remote sensing;
- providing a platform for sharing experiences and lessons learned; and
- promoting collaboration amongst machine learning experts, domain experts, and software developers.
Participants learned the fundamentals of end-to-end machine learning life cycles by designing, implementing, and deploying deep learning models on the cloud. In the process they gained insights into cloud computing for machine learning while in an environment that encouraged collaboration and the exchange of ideas. In addition, IMPACT leveraged NASA’s Space Act Agreement with Amazon to provide Amazon Web Services credits to the participants. The session was facilitated by IMPACT team members: Dr. Manil Maskey, Drew Bollinger, Shubhankar Ghadot, Muthukumaran Ramasubramanian, and Iksha Gurung.
IMPACT plans to offer similar summer school sessions in the future. In the meantime, you can view the session as well as access the slide presentation for this summer school session and the Juypter notebooks used in the workshop.
More information about IMPACT can be found at NASA Earthdata and the IMPACT project website.
