This post is the second one in a series that discusses algorithmic and implementation issues about nonlinear regression using Spark. In the previous post we identified a small window for contribution into Spark MLlib by adding methods for nonlinear regression, starting with the definition and implementation of a general nonlinear model. We remind the reader that regression is essentially an …