In recent years, significant progress has been made in the implementation of decision support systems based on machine learning methods by exploiting very large databases and the use of learning algorithms.

In many research or production environments, the available databases are rarely as large, and the question arises as to whether it makes sense to use machine learning methods in this context.

The ENBIS webinar will introduce transfer learning, which uses knowledge from related applications to implement efficient models with an economy of data.
Several achievements will be presented that successfully use these learning approaches to design machine learning for industrial small data regimes and to develop powerful decision support tools even in cases where the initial data volume is limited.