Production Best Practices: A/B Testing, Drift, Debugging

Introduction: From POC to Production

Your model works in Jupyter. It even works in staging. But production is a different beast: millions of users, malicious actors, unexpected edge cases, and zero tolerance for downtime.

Production ML requires thinking beyond accuracy: security, reliability, scalability, cost, and maintainability all matter.

Key Insight: Building production ML systems is 10% ML and 90% software engineering, infrastructure, and operational excellence.

Learning Objectives

• Implement security best practices
• Design for reliability and fault tolerance
• Build scalable serving infrastructure
• Optimize costs
• Handle edge cases and errors gracefully
• Establish incident response procedures
• Create comprehensive documentation

1. Security Best Practices

Input Validation

Never trust user input! Validate everything:

Rate Limiting

2. Error Handling and Graceful Degradation

3. Monitoring and Alerting

4. Cost Optimization

5. Incident Response Playbook

Key Takeaways

✅ Security first: Validate inputs, rate limit, protect against attacks

✅ Reliability: Implement fallbacks, handle errors gracefully

✅ Monitoring: Track metrics, set alerts, investigate anomalies

✅ Cost optimization: Choose right infrastructure, scale appropriately

✅ Incident response: Have playbooks ready, practice regularly

✅ Documentation: Document everything – architecture, decisions, procedures

Congratulations! 🎉

You've completed the ML Advanced Course! You now have the skills to:

• Build sophisticated unsupervised learning systems
• Develop and train deep neural networks
• Deploy ML models to production at scale
• Implement MLOps best practices
• Optimize models for performance and cost
• Handle real-world production challenges

Next steps:

1. Apply these techniques to real projects
2. Contribute to open-source ML projects
3. Stay updated with latest ML research
4. Share your knowledge with the community

Keep learning, keep building, and remember: Production ML is a journey, not a destination!