Building Production ML Systems: Lessons from 15 Years
After building ML systems for over 15 years, I've learned that the hardest part isn't the algorithms—it's everything else. Here's what they don't teach you in research papers.
The Data Pipeline is Everything
Your model is only as good as your data pipeline. I've seen brilliant algorithms fail in production because the data infrastructure couldn't keep up. Before you write a single line of ML code, ask yourself:
- How will data flow from source to model?
- What happens when upstream systems change?
- How do you detect data quality issues?
- Can you version your datasets?
Key Insight
Spend 80% of your time on data infrastructure, 20% on models. The best algorithm can't fix bad data.
Monitoring Matters More Than Metrics
Accuracy on your test set is nice. Knowing your model is degrading in production is critical. Real-world data drifts, user behavior changes, and upstream systems break. Your monitoring needs to catch:
// Example monitoring check
if (predictionConfidence < THRESHOLD) {
alert("Model confidence dropping");
logFeatureDistribution();
checkDataDrift();
}Set up alerts for distribution shifts, prediction confidence, latency spikes, and error rates. Better to know about problems before your users do.
Deployment is Not the Finish Line
Shipping v1 is just the beginning. Plan for:
- Model retraining cadence
- A/B testing framework
- Rollback procedures
- Feature store management
- Feedback loops from users
This is part of a series on practical ML engineering. Next up: debugging production ML systems.