After implementing ML systems at JP Morgan Chase and deploying Gemini 2.0 models for fraud detection at Majority, I've learned that moving from prototype to production is often the hardest part. Here are the key lessons that matter most.
Production ML systems spend 90% of their complexity on infrastructure, monitoring, and operational concerns, not the ML model itself.
Start Simple, Scale Smart
Begin with rule based systems, then gradually add ML components. At Majority, our fraud detection started with basic heuristics before incorporating sophisticated ML models. This approach lets you understand your data and edge cases before adding complexity.
Monitor Everything
ML models degrade over time. Track model performance, data drift, and business impact continuously. We monitor:
- Model accuracy and response times
- Input data quality and distribution changes
- Business metrics like fraud prevention rates
- System health and error rates
Plan for Failure
Always have fallback mechanisms. When our primary fraud detection model fails, we automatically fall back to our previous model version, then to rule based systems if needed. This ensures business continuity even during system issues.
Focus on Data Quality
Poor data quality kills ML systems faster than poor algorithms. Implement automated data validation, anomaly detection, and quality checks throughout your pipeline. Clean, consistent data is more valuable than complex models.
Optimize for Business Impact
Model accuracy matters less than business outcomes. Our fraud detection system prioritizes minimizing false positives (which frustrate users) while maintaining high catch rates. Always align ML metrics with business objectives.
Test Thoroughly
ML systems require different testing approaches. Beyond unit tests, implement:
- Model behavior tests on known datasets
- Shadow testing alongside production systems
- A/B testing for gradual rollouts
- Edge case and failure scenario testing
Keep It Maintainable
Simple, well documented systems beat complex ones. Use microservices for individual models, implement clear data pipelines, and maintain comprehensive monitoring dashboards. Your future self (and team) will thank you.
The most successful ML deployments focus on building robust, maintainable systems rather than optimizing for the last percentage point of accuracy.
Key Success Factors
- Start with business problems, not ML solutions
- Invest in monitoring and observability early
- Plan for model retraining and updates
- Maintain human oversight and intervention capabilities
- Document everything, especially data assumptions
Production ML is more about engineering discipline than algorithmic sophistication. Focus on reliability, maintainability, and business impact—the models can always be improved later.