coreml

To load a Core ML model and run predictions in your iOS app, use auto-generated Swift classes or manually construct MLFeatureProvider inputs for .mlmodelc or .mlpackage files. This enables direct on-device machine learning inference without requiring network calls.

Yes, you can configure Core ML compute units to optimize model performance and energy efficiency on iOS devices. You can assign inference workloads to the CPU, GPU, or Neural Engine individually or combined, balancing execution speed against power consumption based on your app's requirements.

On-device machine learning handles multi-model pipelines in Swift by chaining sequential Core ML model requests together. You can route outputs from one model as inputs to another using MLFeatureProvider, enabling complex multi-stage inference workflows like object detection followed by classification entirely locally.

The best way to manipulate input data for Core ML models is using MLTensor and MLMultiArray. These structures provide flexible data handling and interoperability, allowing you to transform multi-dimensional image or text data into the exact input format your specific machine learning model expects for inference.

Yes, VNCoreMLRequest works with Core ML models to perform image classification and other computer vision tasks. It wraps your Core ML model within the Vision framework, automatically handling image preprocessing and camera frame integration to streamline on-device visual inference workflows.