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.

Our initial prototype is complete. There's plenty left to be done such as model validation, tests to confirm expected performance after import to iOS and a sleeker/more friendly UI. Nonetheless, I hope this serves as a useful reference for your mobile machine learning deployment endeavors. New and improved tools continue to make mobile pursuits more widely accessible to the data science community and there are many creative opportunities waiting to be claimed in the mobile space. As mobile technology is inherently multi-media, it provides a richness of data types (e.g.

This article describes the shortest path from training a python machine learning model to a proof of concept iOS app you can deploy on an iPhone. The goal is to provide the basic scaffolding while leaving room for further customization suited to one's specific use case. In the spirit of simplicity, we will overlook some tasks such as model validation and building a fully polished user interface (UI). By the end of this tutorial, you will have a trained model running on iOS that you can showcase as a prototype and load to your device. First, let's create a python virtual environment called .core_ml_demo and then install the necessary libraries i.e. pandas scikit-learn and coremltools.

Once your machine learning model is predicting accurate results, you can expose it to the outside world. This article presents the easy-to-follow steps which we can follow to host a machine learning model to the outside world. Public can then access your work via their web browsers. It is an important step for anyone who wants to make a business out of their machine learning models. Think of Flask as a collection of software packages which can help you easily create a web application.