Use Core ML to integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to train or fine-tune models, all on the user’s device.

Core ML optimizes on-device performance by leveraging the CPU, GPU, and Neural Engine while minimizing its memory footprint and power consumption. Running a model strictly on the user’s device removes any need for a network connection, which helps keep the user’s data private and your app responsive.

You can convert a trained XGBoost model to Core ML format.

# Convert it with default input and output names
import coremltools as ct
coreml_model = ct.converters.xgboost.convert(model)

# Saving the Core ML model to a file.'my_model.mlmodel')

For more information, see the API reference.

Updated about a month ago


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