  A number of popular systems, most notably Google's TensorFlow, have been
implemented from the ground up to support machine learning tasks. We consider
how to make a very small set of changes to a modern relational database
management system (RDBMS) to make it suitable for distributed learning
computations. Changes include adding better support for recursion, and
optimization and execution of very large compute plans. We also show that there
are key advantages to using an RDBMS as a machine learning platform. In
particular, learning based on a database management system allows for trivial
scaling to large data sets and especially large models, where different
computational units operate on different parts of a model that may be too large
to fit into RAM.
