Towards Active Learning Based Smart Assistant for Manufacturing
  A general approach for building a smart assistant that guides a user from a
forecast generated by a machine learning model through a sequence of
decision-making steps is presented. We develop a methodology to build such a
system. The system is demonstrated on a demand forecasting use case in
manufacturing. The methodology can be extended to several use cases in
manufacturing. The system provides means for knowledge acquisition, gathering
data from users. We envision active learning can be used to get data labels
where labeled data is scarce.
