  The practical application of machine learning and data science (ML/DS)
techniques present a range of procedural issues to be examined and resolve
including those relating to the data issues, methodologies, assumptions, and
applicable conditions. Each of these issues can present difficulties in
practice; particularly, associated with the manufacturing characteristics and
domain knowledge. The purpose of this paper is to highlight some of the
pitfalls that have been identified in real manufacturing application under each
of these headings and to suggest protocols to avoid the pitfalls and guide the
practical applications of the ML/DS methodologies from predictive analytics to
prescriptive analytics.
