Deploy Edge Ai Model
Skill Verified ActiveDeploy 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, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity constraints.
To enable developers to efficiently deploy and optimize machine learning models on resource-constrained edge devices, overcoming limitations of cloud inference.
Features
- Model quantization (INT8/INT4)
- On-device LLM inference (Gemma 4)
- Android/iOS deployment via AI Edge Gallery
- Hardware delegate selection (GPU/NPU/DSP)
- Performance benchmarking on constrained devices
Use Cases
- Deploying LLMs to mobile phones
- Running AI on IoT devices
- Optimizing inference for embedded systems
- Benchmarking model performance on target hardware
Non-Goals
- Cloud-based model serving
- General machine learning model training
- Building complex UI frameworks for edge apps
Installation
/plugin install agent-almanac@pjt222-agent-almanacQuality Score
VerifiedTrust Signals
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