Tomâs looking for thoughts on geospatial ML frameworks
Cindy: working on global biomass with Joe, currently training xgboost model with input pipeline using the pangeo suite. Looking to expand xbatcher to complete the transition from xarray/dask â ML model.Â
Tom: maybe ML project with Ryan on parameterization of ocean turbulence - interested in state of pangeo ML tools
Stephen: from informatics lab, about to submit a paper on ML estimation of metadata for ocean temperature, QA for ML projects at the met office
Douglas: developing training materials for ML applications within NOAA, also focusing on AI ready data; 3rd NOAA AI workshop(09/13 â 09/17) is accepting abstract until June 18(https://2021noaaaiworkshop.sched.com).
Probably depends on target audience: prototyping / development maybe want flexibility. Newcomers / established production process might benefit from theâframeworkâ-style.
Also concerns about longevitity of new projects(for use and teaching).
âStandardâ scikit-learn vs. Deep learning. For DL models, the input format might change(itâs not just observations x features). Maybe mask handling changes.
Nice to be able to swap out components.
Anything unique about high-dimensional data?
The rows x features layout loses the fact that values inside an n-d grid are related.
June 7
May 3