INTROBLOCK · 01
MOB · 7 MIN PREVIEW
AI for Mobile Engineers
On-device inference. iOS Core ML, Android NNAPI/LiteRT. Streaming over flaky networks. Cache + invalidate sanely.
CONCEPTBLOCK · 02
Mobile AI is a latency + privacy negotiation
Every mobile AI feature lives on a sliding scale: cloud (cheap to ship, slow on bad networks, leaks data) ↔ on-device (instant, private, model-size-bounded). The interesting work is hybrid: keep the cloud for the long-tail, ship a small on-device model for the 80% common case. Apple's Core ML and Android's LiteRT (formerly TFLite) let you run quantised models in tens of milliseconds with no network. Your job is to choose what runs where, ship a reasonable cache, and degrade gracefully when the radio drops.
TIPDefault: on-device for low-stakes/personal data, cloud for fresh/long-tail/heavy. Most apps want both, not one.
WATCH OUTBudget the model bundle in your app size review. A 200 MB Core ML model breaks App Store Family Sharing trust.
DIAGRAMBLOCK · 03
Hybrid: on-device first, cloud fallback
Cache-first. On-device default. Cloud only when on-device confidence is low.
CODEBLOCK · 04
iOS Core ML — load + run a model in 12 lines
SWIFT1import CoreML
2
3func classify(_ pixelBuffer: CVPixelBuffer) async throws -> String {
4 let cfg = MLModelConfiguration()
5 cfg.computeUnits = .cpuAndNeuralEngine
6 let model = try MobileNetV2(configuration: cfg)
7 let out = try await model.prediction(image: pixelBuffer)
8 let (label, conf) = (out.classLabel, out.classLabelProbs[out.classLabel] ?? 0)
9 if conf < 0.6 { throw NSError(domain: "lowConf", code: 1) }
10 return label
11}
.cpuAndNeuralEngine routes to the ANE on supported chips. throw on low confidence is the trigger for cloud fallback.
CHEATSHEETBLOCK · 05
Five things to remember
01On-device first; cloud is the long-tail fallback.
02Quantise (int8) before shipping. 4× smaller, ~equal accuracy on most tasks.
03Bundle the model with App Thinning (iOS) / Dynamic Delivery (Android).
04Stream tokens with backpressure. Flaky networks pause, don't fail.
05Cache aggressively keyed by (input hash, model version).
MINIGAME · RAPIDFIRETFBLOCK · 06
True or false: 6 seconds each
Core ML can run on the Apple Neural Engine on supported chips.
CLAIM 1/5 · READY · scroll into view
LESSON COMPLETEBLOCK · 07
Mobile AI mental model: locked.
NEXTOn-device inference: iOS Core ML loader
WHAT YOU'LL WALK AWAY WITH
Real skills, real career delta.
Skills you'll gain
07- Ship Core ML / NNAPI modelsWorking
Outcome from completing the course: ship core ml / nnapi models.
- Stream over flaky networks gracefullyWorking
Outcome from completing the course: stream over flaky networks gracefully.
- Cache + invalidate sanelyWorking
Outcome from completing the course: cache + invalidate sanely.
- On-device inferenceWorking
Covered in lesson sequence — drop-in ready.
- iOS Core MLWorking
Covered in lesson sequence — drop-in ready.
- Android NNAPIWorking
Covered in lesson sequence — drop-in ready.
- Streaming on flaky networksWorking
Covered in lesson sequence — drop-in ready.
Career & income delta
Career moves
- Lead a AI for Mobile Engineers initiative on your team — most orgs have it on the roadmap and few have shipped it.
- Consulting work at $150-300/hr — 'MOB shipped to production' is a sought-after specialty in 2026.
- Move from generic IC to platform/AI-platform team where AI for Mobile Engineers expertise is the entry ticket.
Income impact
- $15-40K bump for senior ICs adding AI for Mobile Engineers to their resume.
- Freelance / consulting demand for the same skill: $150-300/hr in 2026.
- Closing enterprise deals often hinges on demonstrating the production patterns from this course.
Market resilience
- AI for Mobile Engineers is a durable skill across model and framework consolidations.
- Production guardrails (cost caps, observability, audit, evals) carry forward to whatever the 2027 stack is.
- Core patterns transfer to cloud, on-prem, and hybrid deployments.