Machine Learning

Skills for deploying and running machine learning models on Apple devices using CoreML and related frameworks.

Available Skills

CoreML

Deploy custom ML models on-device — model conversion with coremltools, compression (quantization, palettization), stateful models with KV-cache, MLTensor operations, and LLM inference patterns.

ML Training Paths

Pick the right training toolchain before you build — disambiguates Foundation Models adapters, Create ML, MLUpdateTask personalization, coremltools conversion, and MLX, and flags the format and toolchain traps that make a trained artifact fail to load.

iOS ML (deployment overview)

Navigation hub for on-device ML and speech-to-text — routes you to the right skill for model conversion, inference optimization, debugging, or SpeechAnalyzer transcription.

Core AI

Core AI (iOS 27) — the on-device inference framework behind Apple Intelligence, now open to apps. Convert PyTorch models to .aimodel, run them from Swift across CPU, GPU, and Neural Engine, and back a LanguageModelSession with your own model.

Speech

Speech-to-text with SpeechAnalyzer (iOS 26+) — live transcription from microphone, file transcription, custom vocabulary, and language detection.

Available References

• CoreML API Reference – CoreML API reference, MLTensor, coremltools, state management

Available Diagnostics

• CoreML Diagnostics – Model load failures, slow inference, compression accuracy loss

Example Prompts

• "How do I convert a PyTorch model to CoreML?"
• "My CoreML model is too large, how do I compress it?"
• "How do I implement speech-to-text with SpeechAnalyzer?"
• "Model inference is slow, how do I optimize it?"