Deploy a Python Machine Learning Model on iOS

Our goal here is the shortest path from training a python model to a proof of concept iOS app you can deploy on an iPhone. We’ll create the basic scaffolding and leave plenty of room for further customization such as model validation and a more polished UI.

Our goal here is the shortest path from training a python model to a proof of concept iOS app you can deploy on an iPhone. We’ll create the basic scaffolding and leave plenty of room for further customization such as model validation and a more polished UI.

STEP 1. Set up your environment

From the terminal run:

    python3 -m venv ~/.core_ml_demo
    source  ~/.core_ml_demo/bin/activate
    python3 -m pip install \
    pandas==1.1.1 \
    scikit-learn==0.19.2 \
    coremltools==4.0

And install Xcode. -This guide uses Xcode Version 12.3 (12C33) on macOS Catalina 10.15.5.

Xcode

STEP 2. Train a model

We’ll use sklearn’s Boston Housing Price toy datasetto train a linear regression model. For simplicity, we’ll limit the feature space to 3 predictors.

    import pandas as pd
    from sklearn.linear_model import LinearRegression
    from sklearn.datasets import load_boston

    ## Load data
    boston = load_boston()
    boston_df = pd.DataFrame(boston["data"] )
    boston_df.columns = boston["feature_names"]
    boston_df["PRICE"]= boston["target"]

    y = boston_df["PRICE"]
    X = boston_df.loc[:,["RM", "AGE", "PTRATIO"]]

    ## Train a model
    lm = LinearRegression()
    lm.fit(X, y)

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