Machine Learning and Data Science have enjoyed a renaissance due to the availability of increased computational power and larger data sets. Many questions can be now asked and answered, that previously were beyond our scope. This does not translate instantly into new tools that can be used by those not skilled in the field, as many of the issues and traps still exist. In this paper, we look at some of the new tools that we have created, and some of the difficulties that still need to be taken care of during the transition from a project run by an expert, to a tool for the bench chemist.