# 🚀 How to Build Edge AI Applications with Advantech EdgeAI Workflow

**URL:** <https://forum.aim-linux.advantech.com/t/how-to-build-edge-ai-applications-with-advantech-edgeai-workflow/1307>\
**Category:** News & Announcements\
**Tags:** edgeai\
**Created:** [December 15, 2025, 3:11am UTC](https://forum.aim-linux.advantech.com/t/how-to-build-edge-ai-applications-with-advantech-edgeai-workflow/1307 "2025-12-15T03:11:39Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Hsiaoyu\_Li](https://sea2.discourse-cdn.com/flex016/user_avatar/forum.aim-linux.advantech.com/hsiaoyu_li/32/1606_2.png) [@Hsiaoyu\_Li](https://forum.aim-linux.advantech.com/u/Hsiaoyu_Li)\
**Post date:** [December 15, 2025, 3:11am UTC](https://forum.aim-linux.advantech.com/t/how-to-build-edge-ai-applications-with-advantech-edgeai-workflow/1307/1 "2025-12-15T03:11:39Z")

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**How to Build Edge AI Applications with Advantech EdgeAI Workflow**

**A Simple, Hardware-Oriented Guide for Getting Started**

**Advantech’s EdgeAI Workflow on GitHub provides a clear and practical path for customers who want to develop AI applications on edge devices:**

**👉 [https://github.com/ADVANTECH-Corp/EdgeAI\_Workflow](https://apc01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FADVANTECH-Corp%2FEdgeAI_Workflow&data=05%7C02%7CHsiaoyu.Li%40advantech.com.tw%7Ceac39e5e069645c3cfec08de33d2236e%7Ca77d40d9dcba4ddab5715f18e6da853f%7C0%7C0%7C639005174793530588%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=U5imVRp3HtQUdCjph0%2BLOa%2B0dZGpqFVvn2y4SetXsAg%3D&reserved=0)**

**This workflow helps you move smoothly from hardware setup → AI model → optimization → platform tuning → deployment.**

**Below is a simplified overview of how customers can start developing Edge AI solutions.**

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**1️⃣ Prepare Your Hardware Environment**

**Before running AI applications, make sure your device is ready. Depending on the platform—NVIDIA Jetson, Qualcomm QCS/QRB, Hailo, or x86 Edge AI systems—install:**

- **The correct BSP / driver package**

- **The required AI runtime (TensorRT, QNN, HailoRT, OpenVINO, etc.)**

- **Basic Python & system libraries**

**Once the environment is set, the device is ready to take AI workloads.**

* * *

**2️⃣ Choose or Build Your AI Model**

**Customers can start with:**

- **Pre-trained models (YOLO, classification, pose estimation, etc.)**

- **Their own custom-trained AI model**

**Export the model in a common format, typically ONNX.**

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**3️⃣ Optimize & Convert the Model**

**Each hardware platform needs its own optimized model format.  
The EdgeAI Workflow shows how to convert the model into:**

- **TensorRT engine for NVIDIA**

- **DLC for Qualcomm**

- **HEF for Hailo**

- **OpenVINO IR for x86**

**This ensures the model runs efficiently on the device’s accelerator.**

* * *

**4️⃣ Develop the Edge AI Application**

**After conversion, integrate the optimized model into your application.  
The GitHub repo provides simple examples such as:**

- **Object detection**

- **Video inference pipelines**

**These samples show how to load the model, read camera/video input, and run inference on the hardware.**

* * *

**5️⃣ Deploy to the Device**

**Finally, move your AI application to the target device:**

1. **Copy the optimized model**

2. **Install the Edge AI SDK to enable the required AI runtime libraries**

3. **Run the application directly on the device**

4. **Validate performance (FPS, latency, AI device usage, temperature, memory)**

**From here, the customer can scale to their real-world environment.**

* * *

**🎯 What This Workflow Provides**

- **A clear, step-by-step development path**

- **Cross-platform support (Jetson / Qualcomm / Hailo / x86)**

- **Ready-to-use example applications**

- **Faster time-to-market for Edge AI projects**

**This is the simplest way for customers to understand how Advantech hardware + EdgeAI Workflow help them build and deploy real AI solutions at the edge.**

 ![11](https://us1.discourse-cdn.com/flex016/uploads/aim_linux/original/2X/7/7c1ba3a087196d8ce06403e78b281484f7cdd771.jpeg)

 ![22](https://us1.discourse-cdn.com/flex016/uploads/aim_linux/original/2X/a/a9577c6412e8a5d51b13ecf2781d9c028714d91a.jpeg)