  Machine Learning (ML) seems to be one of the most promising solution to
automate partially or completely some of the complex tasks currently realized
by humans, such as driving vehicles, recognizing voice, etc. It is also an
opportunity to implement and embed new capabilities out of the reach of
classical implementation techniques. However, ML techniques introduce new
potential risks. Therefore, they have only been applied in systems where their
benefits are considered worth the increase of risk. In practice, ML techniques
raise multiple challenges that could prevent their use in systems submitted to
certification constraints. But what are the actual challenges? Can they be
overcome by selecting appropriate ML techniques, or by adopting new engineering
or certification practices? These are some of the questions addressed by the ML
Certification 3 Workgroup (WG) set-up by the Institut de Recherche
Technologique Saint Exup\'ery de Toulouse (IRT), as part of the DEEL Project.
