domains / families / software-engineering / coreml

coreml

separate

Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), 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.

When to use

• loading Core ML models
• making predictions
• configuring compute units
• profiling model performance
• Swift integration
• MLTensor usage
• multi-model pipelines
• model deployment

When not to use

• Python-side model conversion
• optimization (quantization, palettization, pruning)
• framework selection

Skill page

Source