How To Create Machine Learning For Ios

A minimalist guide

Deploy a Python Machine Learning Model on your iPhone — Photo by AltumCode on Unsplash

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 utilize example. In the spirit of simplicity, nosotros will overlook some tasks such every bit model validation and edifice a fully polished user interface (UI). By the end of this tutorial, you volition take a trained model running on iOS that you can showcase as a prototype and load to your device.

Step 1. Set up your surround

First, let'due south create a python virtual surroundings called .core_ml_demo and and then install the necessary libraries i.e. pandas scikit-learn and coremltools. From your terminal run:

Side by side we volition install Xcode. Xcode is a development toolkit for Apple products. Note that Xcode is quite big (> 10 Gb). I'd recommend grabbing a cup of coffee or running your install overnight. –Note, this guide uses Xcode Version 12.3 (12C33) on macOS Catalina 10.15.5.

‎Xcode

Step 2. Railroad train a model

Nosotros'll apply scikit-learn's Boston Housing Cost toy dataset to railroad train a linear regression model to predict home cost based on property and socio-economic attributes. Since we're aiming for simplicity, we'll limit the feature space to 3 predictors and use business firm price as our target variable.

Stride iii. Convert the model to Cadre ML

Apple provides two avenues to develop models for iOS. The first, Create ML, allows 1 to produce models entirely within the Apple tree ecosystem. The 2nd, Core ML, allows one to integrate models from tertiary parties into the Apple platform by converting them to the Core ML format. Since nosotros're interested in running a python trained model on iOS, we'll apply the latter.

We'll convert our sklearn model to the Core ML format (.mlmodel) using python's coremltools package before importing to Xcode. coremltools allows one to assign metadata to a model object such equally authorship data and model feature and result descriptions.

Step 4. Commencement a new Xcode project

And that'southward it for python. From hereon, we tin complete a prototype app using only Xcode and Swift. This tin be done with the setup below.

Open up Xcode and create a new Xcode project
Choose "iOS" equally the Multiplatform type
Select "App" as the Application type

Next, name your project and select the "SwiftUI" Interface.

Now but elevate and driblet the .mlmodel file (saved above in step 3) into your Xcode directory. Xcode will automatically generate a Swift class for your model as shown in the editor below. If you lot inspect your model course, you'll detect that information technology includes the details we entered when saving our python model using coremltools such as feature and target field descriptions. This is handy for model stewardship.

Importing your .coreml file into your Xcode projection

Step 5. Build a model UI

Next we'll build a bones UI by modifying the ContentView.swift file in your Xcode project. The Swift code below sets up a UI that allows users to adjust business firm attributes and then to predict house price. There are several elements we tin review hither.

The NavigationView contains our essential UI. It includes:

Stepper structs (lines 19–thirty) for each of our three features, which enable users to modify characteristic values. Steppers are basically widgets that alter the @State of our house attribute variables (lines 6–viii).
A Button on the navigation bar (lines 31–40) to phone call our model from within the predictPrice part (line 46). This yields an Alert message on the screen with the predicted price.

Exterior of the NavigationView we accept our predictPrice function (lines 46–62). The predictPrice part instantiates our Swift Core ML model class and generates a prediction according the values stored in our feature states.

And at last the fun part. We tin build and run a simulation of our app in Xcode to see our model in activeness. In the example below, I've created a simulation using the iPhone 12.

Conclusion

And that's it! Our initial paradigm is consummate. There's plenty left to be done such every bit 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 proceed to make mobile pursuits more widely accessible to the data science community and there are many artistic opportunities waiting to be claimed in the mobile infinite. As mobile technology is inherently multi-media, it provides a richness of data types (east.g. audio, video, move and location) along with unique bespeak of use applications to aggrandize one's data science toolkit.

As ever, I welcome any feedback or suggestions.

Thanks for reading!

Resource

CoreML

Models – Machine Learning – Apple tree Developer
Quickstart Example

iOS Deployment

Apple tree Programmer Documentation

SwiftUI

SwiftUI by Example

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