With active learning strategies, multiple factors are varied at a time whereas regular DoE strategies often vary a single factor at a time. This makes the post-processing of the experiments less trivial as the effect of the factors is not isolated. As a result, a statistical analysis is required to draw conclusions from the experimental campaign . These tools are incorporated in regular DoE software but not in the active machine learning packages that are available as of today. This is closely related to another issue that limits the applicability, namely its ease-ofuse. The current active machine learning packages require programming skills to be used and have no graphical user interface (GUI). The absence of a GUI hampers the usage of these methodologies as programming expertise is required before they can be applied. There is at present a substantial time investment of the researchers needed to use Active Machine Learning.