peterbamuhigire/ios-ai-ml

iOS AI/ML — On-Device Intelligence with Apple's Stack

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Use When

• On-device AI/ML for iOS using Apple's stack — CoreML model integration (generated wrappers, batch prediction, dynamic model loading), Vision framework (face detection, landmarks, barcode, saliency, image similarity, real-time camera...
• The task needs reusable judgment, domain constraints, or a proven workflow rather than ad hoc advice.

Do Not Use When

• The task is unrelated to ios-ai-ml or would be better handled by a more specific companion skill.
• The request only needs a trivial answer and none of this skill's constraints or references materially help.

Required Inputs

• Gather relevant project context, constraints, and the concrete problem to solve.
• Confirm the desired deliverable: design, code, review, migration plan, audit, or documentation.

Workflow

• Read this SKILL.md first, then load only the referenced deep-dive files that are necessary for the task.
• Apply the ordered guidance, checklists, and decision rules in this skill instead of cherry-picking isolated snippets.
• Produce the deliverable with assumptions, risks, and follow-up work made explicit when they matter.

Quality Standards

• Keep outputs execution-oriented, concise, and aligned with the repository's baseline engineering standards.
• Preserve compatibility with existing project conventions unless the skill explicitly requires a stronger standard.
• Prefer deterministic, reviewable steps over vague advice or tool-specific magic.

Anti-Patterns

• Treating examples as copy-paste truth without checking fit, constraints, or failure modes.
• Loading every reference file by default instead of using progressive disclosure.

Outputs

• A concrete result that fits the task: implementation guidance, review findings, architecture decisions, templates, or generated artifacts.
• Clear assumptions, tradeoffs, or unresolved gaps when the task cannot be completed from available context alone.
• References used, companion skills, or follow-up actions when they materially improve execution.

References

• Use the links and companion skills already referenced in this file when deeper context is needed.

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The Apple AI Stack

CoreML            ← inference engine (all platforms, on-device)
   ↑
Vision            ← image analysis (wraps CoreML)
NaturalLanguage   ← text/language (wraps CoreML)
Speech            ← audio→text
SoundAnalysis     ← audio classification
   ↑
CreateML          ← training (macOS only, Swift + Xcode app)
Turi Create       ← training (Python, open source, Apple-maintained)
CoreML Tools      ← model conversion from Keras/TF/Caffe → CoreML (Python)

CoreML runs entirely on-device — no network required for inference. Selects optimal compute unit automatically: Neural Engine → GPU → CPU.

---

Section 1: CoreML Model Integration

Standard 4-Step Workflow

// Step 1: Add .mlmodel to Xcode project
// Step 2: Xcode auto-generates typed wrapper classes

// Step 3: Create input, call prediction()
let mobileNet = MobileNet()
let input = MobileNetInput(image: pixelBuffer)  // typed input
let output = try mobileNet.prediction(input: input)

// Step 4: Read typed output properties
print(output.classLabel)           // String
print(output.classLabelProbs)      // [String: Double]

Batch Prediction

let inputs: [MobileNetInput] = [input1, input2, input3]
let batchIn = MLArrayBatchProvider(array: inputs)
let batchOut = try mobileNet.model.predictions(from: batchIn)

Dynamic Model Loading (Downloaded Models)

// Compile downloaded model on-device
let compiledUrl = try MLModel.compileModel(at: downloadedModelUrl)

// Move from temp to permanent location
let appSupportDir = FileManager.default.urls(
    for: .applicationSupportDirectory, in: .userDomainMask).first!
let permanentUrl = appSupportDir.appendingPathComponent("MyModel.mlmodelc")
try FileManager.default.moveItem(at: compiledUrl, to: permanentUrl)

let model = try MLModel(contentsOf: permanentUrl)

Low-Level CoreML (Custom/Unusual Models)

let input = try MLDictionaryFeatureProvider(dictionary: [
    "image": MLFeatureValue(pixelBuffer: pixelBuffer)
])
let output = try model.prediction(from: input)
let classLabel = output.featureValue(for: "classLabel")?.stringValue

MLMultiArray for Numeric Inputs

let multiArray = try MLMultiArray(shape: [1, 3, 224, 224], dataType: .float32)
// Fill array values...
let input = MLFeatureValue(multiArray: multiArray)

Compute Unit Configuration

let config = MLModelConfiguration()
config.computeUnits = .cpuOnly  // .all | .cpuAndGPU | .cpuOnly
let model = try MobileNet(configuration: config)
// Use .cpuOnly for debugging/testing; .all in production

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Additional Guidance

Extended guidance for ios-ai-ml was moved to references/skill-deep-dive.md to keep this entrypoint compact and fast to load.

Use that deep dive for:

• Section 2: Vision Framework
• Section 3: Natural Language Framework
• Section 4: CreateML Training Workflow
• Section 5: On-Device Model Updates (Personalization)
• Section 6: Model Optimisation
• Section 7: On-Device vs Cloud AI
• Section 8: Privacy-Preserving Patterns
• Section 9: Anti-Patterns