  Machine learning (ML) is becoming increasingly crucial in many fields of
engineering but has not yet played out its full potential in bioprocess
engineering. While experimentation has been accelerated by increasing levels of
lab automation, experimental planning and data modeling are still largerly
depend on human intervention. ML can be seen as a set of tools that contribute
to the automation of the whole experimental cycle, including model building and
practical planning, thus allowing human experts to focus on the more demanding
and overarching cognitive tasks. First, probabilistic programming is used for
the autonomous building of predictive models. Second, machine learning
automatically assesses alternative decisions by planning experiments to test
hypotheses and conducting investigations to gather informative data that focus
on model selection based on the uncertainty of model predictions. This review
provides a comprehensive overview of ML-based automation in bioprocess
development. On the one hand, the biotech and bioengineering community should
be aware of the potential and, most importantly, the limitation of existing ML
solutions for their application in biotechnology and biopharma. On the other
hand, it is essential to identify the missing links to enable the easy
implementation of ML and Artificial Intelligence (AI) tools in valuable
solutions for the bio-community.
