Course · 4 chapters
On-Device- & Edge-KI
Run AI directly on a phone or Mac with no cloud round-trip. Build with Apple Foundation Models, Gemini Nano, and MLX across 4 advanced chapters for app engineers.
What you'll be able to do
- Embed Apple Foundation Models in iOS apps
- Ship on-device AI to Android with Gemini Nano
- Run local models on Apple Silicon with MLX
- Choose on-device or cloud inference
- Match edge platforms to your use case
What's inside
- 1On-Device & Edge AI: Starte hier
Eine 12-minütige Orientierung zum On-Device & Edge AI Skill Path — die drei Plattformen, auf denen dein Modell ohne Cloud-Umweg laufen kann, und wie du die richtige auswählst.
- 2Apple Foundation Models für Swift-Entwickler
Ein leistungsfähiges ~3B LLM in deiner iOS-App ausliefern — Guided Generation, Tool Calling und wann du trotzdem zu Claude greifen solltest
- 3Gemini Nano und AICore auf Android
Liefere On-Device-KI zu 140M+ Android-Geräten aus — AICore als gemeinsamer Systemdienst, ML Kit GenAI APIs und Gemma 4 agentische Intelligenz.
- 4MLX in der Praxis: Lokale Inferenz auf Apple Silicon
Beliebige Open-Source-Modelle auf deinem Mac ausführen — Unified Memory, Quantisierung die tatsächlich Qualität bewahrt, und die ehrlichen Grenzen eines Single-User-Inferenzknotens
Frequently asked questions
- What will I learn in this on-device AI course?
- You learn to run AI models directly on a device with no cloud call, using Apple Foundation Models in iOS apps, Gemini Nano and AICore on Android, and MLX for local inference on Apple Silicon. The path also covers quantization, tool calling, and when to fall back to a cloud model like Claude.
- Who is this course for?
- It is built for software and AI engineers, especially iOS, Android, and Mac developers who want to add private, low-latency AI features that work without a server. The level is advanced and assumes you already ship production app code.
- Do I need coding experience to take this course?
- Yes. This is an advanced engineering path that works with Swift on iOS, Android system APIs, and Python tooling such as MLX and Ollama, so you should already be comfortable building and running app code.
- How long is the course and is there a certificate?
- The path has 4 chapters totaling about 78 minutes, with a 12-minute orientation and three 22-minute platform chapters on Apple, Android, and Mac. You earn a certificate of completion after finishing every chapter.
- Is this course free?
- No, On-Device & Edge AI is a paid path included in the AI Academy subscription. It sits in the AI for Engineers track alongside the other AI engineering paths.
Earn a certificate
Complete all chapters to receive your certificate of completion.