- On-Device & Edge AI: Start Here
- Apple Foundation Models for Swift Developers
- Gemini Nano and AICore on Android
- MLX in Practice: Local Inference on Apple Silicon
Edge AI Course: On-Device LLMs for App Engineers
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.

Info basate su dati pubblici. I dati potrebbero essere imprecisi o non aggiornati: verificali sul sito del provider. Formazione non è responsabile del corso, organizzato dal provider, che è il solo responsabile dell’iscrizione e dell’erogazione del corso.
In sintesi
Corso da seguire online.
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.
Il programma
- Ore
- a 12-minute orientation and three 22-minute platform chapters
A chi è rivolto
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.
Obiettivi
Embed a ~3B Apple Foundation Models LLM in an iOS app using guided generation and tool calling
Ship on-device AI to Android with AICore and the ML Kit GenAI APIs
Run open models locally on Apple Silicon with MLX, Ollama, and quantization
Decide between on-device inference and a cloud model like Claude for a given task
Pick the right edge platform across Apple, Android, and Mac for a use case
Reason about the throughput and limits of a single-user local inference node
Requisiti di ammissione
you should already be comfortable building and running app code
Certificazione finale
Certificato al completamento
Scheda del corso
- Modalità
- Online
- Durata
- 1,3 ore
- Ente organizzatore
- Anthropos AI Academy
- Lingua
- Italiano
- Livello
- advanced
Domande frequenti
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.

