  The popularity of data science as a discipline and its importance in the
emerging economy and industrial progress dictate that machine learning be
democratized for the masses. This also means that the current practice of
workforce training using machine learning tools, which requires low-level
statistical and algorithmic details, is a barrier that needs to be addressed.
Similar to data management languages such as SQL, machine learning needs to be
practiced at a conceptual level to help make it a staple tool for general
users. In particular, the technical sophistication demanded by existing machine
learning frameworks is prohibitive for many scientists who are not
computationally savvy or well versed in machine learning techniques. The
learning curve to use the needed machine learning tools is also too high for
them to take advantage of these powerful platforms to rapidly advance science.
In this paper, we introduce a new declarative machine learning query language,
called {\em MQL}, for naive users. We discuss its merit and possible ways of
implementing it over a traditional relational database system. We discuss two
materials science experiments implemented using MQL on a materials science
workflow system called MatFlow.
