MLOps Best Practices for Production Systems

Deploying machine learning models to production is just the beginning. Here are key practices I've learned from building production ML systems.

1. Version Everything

• Model versions
• Data versions
• Code versions
• Configuration versions

Use tools like MLflow, DVC, or custom solutions to track all artifacts.

2. Monitor Continuously

Production ML systems need monitoring beyond traditional application metrics:

• Model performance metrics
• Data drift detection
• Prediction latency
• Resource utilization

3. Automate Testing

Implement comprehensive testing:

• Unit tests for data processing
• Integration tests for pipelines
• Model validation tests
• A/B testing in production

4. Design for Rollback

Always have a rollback strategy:

• Blue-green deployments
• Canary releases
• Feature flags for model switching

5. Document Everything

Clear documentation is crucial:

• Model cards
• API documentation
• Deployment procedures
• Troubleshooting guides

Conclusion

MLOps is about bringing software engineering best practices to machine learning. Start with these fundamentals and iterate based on your specific needs.