Suppose we want to use Machine Learning (ML) technologies onboard aircraft. The reasons for doing so range from increasing autonomy and easing pilot workload to simply reducing the computing resources required by avionics. Can these benefits be realized while maintaining or even improving aircraft safety? What are the barriers for ML assurance and certification, and how might they be overcome? Researchers, regulators, and the aviation industry have been working to answer these questions, developing new guidance for certification of ML-based airborne systems, such as the ED-324 / ARP6983 standard that is currently being developed by EUROCAE WG-114 / SAE G-34 joint working group. This keynote will discuss several key challenges in the assurance of safety-critical ML-enabled components that are addressed by the working group. We will then highlight new technologies and processes that are being created to meet corresponding certification objectives. Specifically, formal methods will play a large role in the safety assurance and deployment of ML onboard aircraft.