Deploying machine learning models to production is fundamentally different from building them in a notebook. Over the past year, our team has productionized several ML systems — here’s what we’ve learned.
The Data Pipeline
Every ML system starts with data. We’ve found that investing in robust data pipelines pays dividends:
- Feature stores prevent duplicate work across teams
- Data validation catches drift before it affects models
- Lineage tracking makes debugging possible
Model Serving Strategies
There’s no one-size-fits-all approach to serving models:
| Approach | Latency | Throughput | Complexity |
|---|---|---|---|
| REST API | ~10ms | Medium | Low |
| gRPC | ~5ms | High | Medium |
| Batch | ~minutes | Very High | Low |
| Streaming | ~100ms | High | High |
Monitoring in Production
The work doesn’t stop at deployment. Continuous monitoring is essential:
- Data drift — Input distributions change over time
- Concept drift — The relationship between features and target shifts
- Performance degradation — Model accuracy declines
Key Takeaways
After several production deployments, our key lessons are:
- Start with a simple model and excellent data
- Invest in monitoring before the first deployment
- Build for iteration — the first version won’t be the last
- Automate retraining pipelines from day one
AI in production is still an emerging discipline, but the organizations that get the fundamentals right will have a significant advantage.