Secure ML Deployment Guide 🛡️

Machine learning models, once deployed, can become targets for attacks. Here's a comprehensive guide to securing your ML systems:

Key Security Practices 🔐

• Data Encryption
  Use TLS for data in transit and AES-256 for data at rest.

  

• Model Protection
  Implement model obfuscation techniques like TensorFlow Secure Predict to prevent reverse engineering.

  

• Secure Containerization
  Deploy models in isolated environments using Docker with restricted privileges.

  

Deployment Checklist 🧭

• Validate all input data with schema validation tools
• Use private registries for model artifacts
• Enable rate limiting to prevent DDoS attacks
• Monitor for anomalies via real-time logging

Advanced Topics 🚀

For deeper insights, check our Advanced Security Topics guide covering:

• Federated learning frameworks
• Hardware security modules (HSMs)
• Zero-trust architecture integration

Stay secure! 🌐