Practical Examples and Developer Guidelines 🛠️

Practical Use of MCP:

• Serialize your trained model using ONNX:

import onnx
# Export model
onnx_model = onnx.load('model.pth')
# Save with MCP protocol considerations
onnx.save(onnx_model, 'model.onnx')

• Deploy Model Securely:

scp model.onnx user@edge-device:/models/

Practical Use of API:

• Create a REST endpoint with Flask:

from flask import Flask, jsonify
app = Flask(__name__)

@app.route('/api/status')
def status():
    return jsonify({'status': 'ok', 'version': '1.0'})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

• Call API from a client:

import requests
response = requests.get('http://server:8080/api/status')
print(response.json())

Guidelines:

• Choose MCP for model portability and integrity.
• Use API for interactive, real-time data exchange.
• Always secure sensitive communication.