How to train your first machine learning model and run it inside your iOS app via CoreML
Machine Learning (ML) in the context of mobile apps is a wide topic, with different types of implementations and requirements. On the highest levels, you can distinguish between:
- Running ML models on server infrastructure and accessing it from your app through API requests
- Running ML models on-device within your app (we will focus on this)
- Fine-tuning pre-trained ML models on-device based on user behavior
- Training new ML models on-device
As part of this blog series, we will be talking about variant 2: We start out by training a new ML model on your server infrastructure based on real-life data, and then distributing and using that model within your app. Thanks to Apple’s CoreML technology, this process has become extremely efficient & streamlined.
We wrote this guide for all developers, even if you don’t have any prior data science or backend experience.
Step 1: Collecting the data to train your first ML model
To train your first machine learning model, you’ll need some data you want to train the model on. In our example, we want to optimize when to show certain prompts or messages in iOS apps.
Let’s assume we have your data in the following format:
- Outcome describes the result of the user interaction, in this case, if they purchased an optional premium upgrade
- Battery Level is the user’s current battery level as a float
- Phone Charging defines if the phone is currently plugged in as a boolean
In the above example, the “label” of the dataset is the outcome. In machine learning, a label for training data refers to the output or answer for a specific instance in a dataset. The label is used to train a supervised model, guiding it to understand how to classify new, unseen examples or predict outcomes.
How you get the data to train your model is up to you. In our case, we’d collect non-PII data just like the above example, to train models based on real-life user behavior. For that we’ve built out our own backend infrastructure, which we’ve already covered in our Blog:
- Building the Infrastructure to Ingest 40m Context Events per Day
- Unifying Data Models Across a Heterogeneous Stack
Step 2: Load and prepare your data
There are different technologies available to train your ML model. In our case, we chose Python, together with pandas and sklearn.
Load the recorded data into a pandas DataFrame:
import pandas as pdrows = [ ['Dismissed', 0.90, False], ['Dismissed', 0.10, False], ['Purchased', 0.24, True], ['Dismissed', 0.13, True]]data = pd.DataFrame(rows, columns=['Outcome', 'Battery Level', 'Phone Charging?'])print(data)
view rawload_and_prepare_data.py hosted with ❤ by GitHub
Instead of hard-coded data like above, you’d access your database with the real-world data you’ve already collected.
Step 3: Split the data between training and test data
To train a machine learning model, you need to split your data into a training set and a test set. We won’t go into detail about why that’s needed, since there are many great resources out there that explain the reasoning, like this excellent CGP Video.
from sklearn.model_selection import train_test_splitX = data.drop("Outcome", axis=1)Y = data["Outcome"]X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, shuffle=True)
view rawsplit_data.py hosted with ❤ by GitHub
The code above splits your data by a ratio of 0.2 (⅕) and separates the X and the Y axis, which means separating the label (“Outcome”) from the data (all remaining columns).
Step 4: Start Model Training
As part of this step, you’ll need to decide on what classifier you want to use. In our example, we will go with a basic RandomForest classifier:
from sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import classification_reportclassifier = RandomForestClassifier()classifier.fit(X_train, Y_train)Y_pred = classifier.predict(X_test)print(classification_report(Y_test, Y_pred, zero_division=1))
view rawmodel_training.py hosted with ❤ by GitHub
The output of the above training will give you a classification report. In simplified words, it will tell you more of how accurate the trained model is.
In the screenshot above, we’re only using test data as part of this blog series. If you’re interested in how to interpret and evaluate the classification report, check out this guide).
Step 5: Export your model into a CoreML file
Apple’s official CoreMLTools make it extremely easy to export the classifier (in this case, our Random Forest) into a .mlmodel (CoreML) file, which we can run on Apple’s native ML chips. CoreMLTools support a variety of classifiers, however not all of them, so be sure to verify its support first.
import coremltoolscoreml_model = coremltools.converters.sklearn.convert(classifier, input_features="input")coreml_model.short_description = "My first model"coreml_model.save("MyFirstCustomModel.mlmodel")
view rawexport_model.py hosted with ❤ by GitHub
Step 6: Bundle the CoreML file with your app
For now, we will simply drag & drop the CoreML file into our Xcode project. In a future blog post we will go into detail on how to deploy new ML models over-the-air.
Once added to your project, you can inspect the inputs, labels, and other model information right within Xcode.
Step 7: Executing your Machine Learning model on-device
Xcode will automatically generate a new Swift class based on your .mlmodel file, including the details about the inputs, and outputs.
let batteryLevel = UIDevice.current.batteryLevellet batteryCharging = UIDevice.current.batteryState == .charging || UIDevice.current.batteryState == .fulldo { let modelInput = MyFirstCustomModelInput(input: [ Double(batteryLevel), Double(batteryCharging ? 1.0 : 0.0) ]) let result = try MyFirstCustomModel(configuration: MLModelConfiguration()).prediction(input: modelInput) let classProbabilities = result.featureValue(for: "classProbability")?.dictionaryValue let upsellProbability = classProbabilities?["Purchased"]?.doubleValue ?? -1 print("Chances of Upsell: \(upsellProbability)")} catch { print("Error running CoreML file: \(error)")}
view rawModelRunner.swift hosted with ❤ by GitHub
In the above code you can see that we pass in the parameters of the battery level, and charging status, using an array of inputs, only identified by the index. This has the downside of not being mapped by an exact string, but the advantage of faster performance if you have hundreds of inputs.
Alternatively, during model training and export, you can switch to using a String-based input for your CoreML file if preferred.
We will talk more about how to best set up your iOS app to get the best of both worlds, while also supporting over-the-air updates, dynamic inputs based on new models, and how to properly handle errors, process the response, manage complex AB tests, safe rollouts, and more.
Conclusion
In this guide we went from collecting the data to feed into your Machine Learning model, to training the model, to running it on-device to make decisions within your app. As you can see, Python and its libraries, including Apple’s CoreMLTools, make it very easy to get started with your first ML model. Thanks to native support of CoreML files in Xcode, and executing them on-device, we have all the advantages of the Apple development platform, like inspecting model details within Xcode, strong types and safe error handling.
In your organization, you’ll likely have a Data Scientist who will be in charge of training, fine-tuning and providing the model. The above guide shows a simple example - with ContextSDK we take more than 180 different signals into account, of different types, patterns, and sources, allowing us to achieve the best results, while keeping the resulting models small and efficient.
Within the next few weeks, we will be publishing a second post on that topic, showcasing how you can deploy new CoreML files to Millions of iOS devices over-the-air within seconds, in a safe & cost-efficient manner, managing complicated AB tests, dynamic input parameters, and more.
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