You've got a ton of data to analyze and you need to track your project end-to-end – from data pre-processing, through model optimization and evaluation, to deployment and analysis. And, perhaps you also have scores of simulations for which their outputs, corresponding parameters, package dependencies, and source code need to be tracked and stored all in one place. Sound familiar? Join us for a demonstration of MLFlow, a platform-agnostic and collaboration-friendly machine learning lifecycle manager that enables seamless teamwork where each member's individual progress can be tracked and reproduced. Presenters Artin Majdi and Ariyan Zarei will demonstrate how they've applied MLFlow to a PhytoOracle modeling project using ag-based remote sensing data to enable a robust, efficient, and reproducible ML pipeline that helps you get the job done and done right.

Please also see an earlier webinar about the PhytoOracle project.

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This material is based upon work supported by the National Science Foundation under Grant Nos. DBI-0735191, DBI-1265383, and DBI-1743442.

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