I recently worked on a project, where I needed to train a Machine Learning model that would run on the Edge -- meaning, the processing and prediction occur on the device that collects the data. As usual, I did my Machine Learning part in Python and I haven't thought much about how we're going to port my ML stuff to the edge device, which was written in Java. When the modeling part was nearing the end, I started researching how to load a LightGBM model in Java. Prior to this, I had a discussion with a colleague who recommended that I retrain the model with the XGBoost model, which can be loaded in Java with XGBoost4J dependency. LightGBM and XGBoost are both gradient boosting libraries with a few differences.

If you generate machine learning (ML) models, you know that the key challenge is exporting and importing them into other frameworks to separate model generation and prediction. Many applications use PMML (Predictive Model Markup Language) to move ML models from one framework to another. PMML is an XML representation of a data mining model. In this post, I show how to build a PMML application on AWS. First, you build a PMML model in Apache Spark using Amazon EMR.