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

White Paper Machine Learning in Certified Systems