  Fostered by novel analytical techniques, digitalization and automation,
modern bioprocess development provides high amounts of heterogeneous
experimental data, containing valuable process information. In this context,
data-driven methods like machine learning (ML) approaches have a high potential
to rationally explore large design spaces while exploiting experimental
facilities most efficiently. The aim of this review is to demonstrate how ML
methods have been applied so far in bioprocess development, especially in
strain engineering and selection, bioprocess optimization, scale-up, monitoring
and control of bioprocesses. For each topic, we will highlight successful
application cases, current challenges and point out domains that can
potentially benefit from technology transfer and further progress in the field
of ML.
