Aiding Machine Learning with FME

In NLP-based street address parsing, training new rules to enhance existing models is as much a dicey proposition as a necessity. The problem is that the additions of the newly-trained rules may unexpectedly nullify previously-trained-and-established rules in the model and may lead to a lower overall success rate. The presenter will share a training and evaluation system built with FME Server at its core supplemented with Google Data Studio for monitoring and assessment to maximize the NLP training performance and outcome.