Edge AI for the Web

Introduction

Edge AI refers to the deployment of artificial intelligence algorithms directly on edge devices. This lesson explores how Edge AI can be integrated into web development, enhancing user experiences by processing data closer to the source.

Key Concepts

Implementation

To implement Edge AI in web applications, you can follow these steps:

1. Identify the Use Case:
2. Select Edge AI Frameworks:
3. Integrate with Web Technologies:
4. Deploy on Edge Devices:
5. Monitor and Optimize:

Example: Using TensorFlow.js

Below is a simple example of how to use TensorFlow.js for running a model on the client side:

                    // Load the model
                    const model = await tf.loadLayersModel('model.json');
                    
                    // Preprocess the input
                    const inputTensor = tf.tensor([imageData]);
                    
                    // Make predictions
                    const predictions = model.predict(inputTensor);
                

Best Practices

When working with Edge AI, keep these best practices in mind:

• Optimize AI Models for Performance
• Ensure Data Security and Privacy
• Use Progressive Web Apps for Offline Capabilities
• Test on Multiple Edge Devices

FAQ