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.

Core ML is a machine learning (ML) model format created and supported by Apple that compiles, deploys, and runs on Apple devices. Developers who train their models in popular frameworks such as TensorFlow and PyTorch convert models to Core ML format to deploy them on Apple devices. Neo is an ML model compilation service on AWS that enables you to automatically convert models trained in TensorFlow, PyTorch, MXNet, and other popular frameworks, and optimize them for the target of your choice. With the new automated model conversion to Core ML, Neo now makes it easier to build apps on Apple's platform to convert models from popular libraries like TensorFlow and PyTorch to Core ML format. In this post, we show how to set up automatic model conversion, add a model to your app, and deploy and test your new model.