Load, configure, and run Core ML models in iOS apps. This skill covers the Swift side: model loading, prediction, MLTensor, profiling, and deployment. Target iOS 26+ with Swift 6.2, backward-compatible to iOS 14 unless noted.
Scope boundary: Python-side model conversion, optimization (quantization, palettization, pruning), and framework selection live in the apple-on-device-ai skill. This skill owns Swift integration only.
See references/coreml-swift-integration.md for complete code patterns including actor-based caching, batch inference, image preprocessing, and testing.
Integrate and optimize Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodelc, .mlpackage), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance. Source: dpearson2699/swift-ios-skills.