Whitepaper #128 submitted to the Planetary Science and Astrobiology Decadal Survey 2023-2032. Topics: state of the profession; technology development; theory, computation, and modeling
Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To support these methods, we propose ten recommendations for bolstering a data-rich future in planetary science.