Load Core ML models and run predictions in Swift iOS apps.
npx skills add https://github.com/harshav167/build-ios-apps --skill coreml-harshav167
Please help me install this Agent Skill. Skill: coreml Source: https://github.com/harshav167/build-ios-apps/tree/main/skills/coreml Command: npx skills add https://github.com/harshav167/build-ios-apps --skill coreml-harshav167
## What problem does it solve? Core ML on-device inference is essential for private, low-latency AI in iOS apps; this skill centralizes Swift-based Core ML integration to streamline model loading, prediction, and deployment.
## Core Features & Use Cases
### Quick Start Load a Core ML model, configure compute units, and run a sample prediction to validate on-device inference.
High-intent search queries and answers about installing and using this skill.
To load a Core ML model in Swift, use auto-generated classes or MLFeatureProvider from .mlmodel, .mlpackage, or .mlmodelc files. You can then configure compute units and execute on-device inference predictions directly within your iOS app.
Core ML compute units like CPU, GPU, and Neural Engine can be configured during model initialization. You can also leverage MLComputePlan for profiling to optimize on-device inference deployment and prediction performance across different hardware units.
Yes, you can integrate Core ML models with the Vision framework for automatic image preprocessing. This allows you to build multi-model pipelines that handle on-device inference workflows across your iOS apps efficiently.
Efficient Core ML deployment involves implementing caching, background loading, and memory considerations. Managing these aspects ensures low-latency, private on-device inference and prediction execution without degrading iOS app performance.
Core ML on-device inference supports both MLMultiArray and MLTensor workflows in Swift. These structures handle input and output data for model predictions, integrating seamlessly with MLFeatureProvider and auto-generated classes.
MLComputePlan is used during Core ML integration to profile and evaluate compute unit performance. It helps optimize model loading and prediction execution by analyzing CPU, GPU, and Neural Engine capabilities before deployment.
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