  Machine Learning (ML) is increasingly used to implement advanced applications
with non-deterministic behavior, which operate on the cloud-edge continuum. The
pervasive adoption of ML is urgently calling for assurance solutions assessing
applications non-functional properties (e.g., fairness, robustness, privacy)
with the aim to improve their trustworthiness. Certification has been clearly
identified by policymakers, regulators, and industrial stakeholders as the
preferred assurance technique to address this pressing need. Unfortunately,
existing certification schemes are not immediately applicable to
non-deterministic applications built on ML models. This article analyzes the
challenges and deficiencies of current certification schemes, discusses open
research issues, and proposes a first certification scheme for ML-based
applications.
