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ML Field Planner

The ML Field Planner is a framework for analyzing ML pipelines and studying edge-to-center tradeoffs regarding function placement of ML. Using ML Field Planner, researchers configure experiments to run on real IoT hardware, configure machine learning models to analyze custom benchmark datasets, and experiment with different algorithm configurations, such as storage compression, all from a graphical user interface.

Please cite the following paper if you use this tool in your research: Joe Stubbs, Sowbaranika Balasubramaniam, Samuel Khuvis, Sachith Withana, Manikya Swathi Vallabhajosyula, Richard Cardone, Christian Garcia, Nathan Freeman, Carlos Guzman, Beth Plale, Rajiv Ramnath, and Tanya Berger-Wolf. 2025. ML Field Planner: Analyzing and Optimizing ML Pipelines For Field Research. In Practice and Experience in Advanced Research COMPuting 2025: The Power of Collaboration (PEARC '25). Association for COMPuting Machinery, New York, NY, USA, Article 8, 1–9. https://doi.org/10.1145/3708035.3736013

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Acknowledgements​

This work has been funded by grants from the National Science Foundation, including the ICICLE AI Institute (OAC 2112606) and Tapis (OAC 1931439).