edge-deployment
CommunityDeploy ML models to edge devices.
Software Engineering#optimization#machine learning#embedded systems#edge deployment#tensorrt#core ml#tensorflow lite
Authordoanchienthangdev
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill addresses the complexities of deploying machine learning models to resource-constrained edge devices, enabling on-device inference and reducing reliance on cloud connectivity.
Core Features & Use Cases
- Model Optimization: Convert and optimize models for various edge platforms like TensorFlow Lite, Core ML, and TensorRT.
- Platform Support: Covers deployment strategies for mobile (iOS/Android), embedded systems (Raspberry Pi, Jetson), and microcontrollers.
- Use Case: Deploy a real-time object detection model on a Raspberry Pi for an IoT security camera, ensuring low latency and offline operation.
Quick Start
Use the edge-deployment skill to convert a TensorFlow model to TensorFlow Lite with FP16 quantization.
Dependency Matrix
Required Modules
None requiredComponents
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: edge-deployment Download link: https://github.com/doanchienthangdev/omgkit/archive/main.zip#edge-deployment Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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