  We study machine learning systems for real-time industrial quality control.
In many factory systems, production processes must be continuously controlled
in order to maintain product quality. Especially challenging are the systems
that must balance in real-time between stringent resource consumption
constraints and the risk of defective end-product. There is a need for
automated quality control systems as human control is tedious and error-prone.
We see machine learning as a viable choice for developing automated quality
control systems, but integrating such system with existing factory automation
remains a challenge. In this paper we propose introducing a new fog computing
layer to the standard hierarchy of automation control to meet the needs of
machine learning driven quality control.
