deploy-edge-ai-model
Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices,
By pjt222, quoted from its SKILL.md as written.
What it is
- Repository
- pjt222/agent-almanac
- Folder
- skills/deploy-edge-ai-model
- Category
- Infrastructure and DevOps
- Licence
- MIT
- Repository last updated
- Nov 6, 2026
- Copies found in other repositories
- 9
- Scripts beside the SKILL.md
- 0
What its code touches
This skill is instructions only. It ships no scripts, so there is no code to run.
Read from the text of its scripts, so this shows what the code can do, not what it does on any one run. Nothing was run.
Checks
- YesReads cleanly. Its header carries a name and a description.
- YesLicence known. A licence file names the terms: MIT.
- YesWhat it touches is recorded. Read from its scripts on Nov 8, 2026.
- Not yetInstalls. The install test in a clean, offline sandbox has not run on this skill yet.
- YesStill maintained. Its repository changed 2 days before it was read.
The Monitor indexes this skill and links to it. The files live in the author's repository, and the name and description are the author's own words. An author can correct or remove this page through Right of reply.