Many ML projects never make it past the experimental phase. The gap between notebook and production is wider than most teams expect. Here's how to bridge it.
The Production Challenge
Moving from Jupyter notebook to production involves:
- Reproducibility: Can you recreate the exact model?
- Scalability: Will it handle production load?
- Monitoring: How do you detect degradation?
- Updates: Can you deploy new versions safely?
Essential MLOps Components
1. Model Registry
Track model versions, metadata, and artifacts:
import mlflow
# Log model with metadata
mlflow.sklearn.log_model(
model,
"model",
registered_model_name="customer_churn_predictor"
)
2. Feature Store
Centralize feature engineering:
- Consistent features across training and serving
- Reuse features across models
- Track feature lineage
3. Model Serving
Deploy with proper infrastructure:
- REST API or gRPC endpoints
- Auto-scaling capabilities
- Low-latency inference
- A/B testing support
4. Monitoring
Track model performance in production:
- Prediction latency
- Model accuracy metrics
- Data drift detection
- Feature distribution changes
Deployment Patterns
Blue-Green Deployment
Maintain two environments:
# Deploy new version
- Deploy model v2 to green environment
- Run validation tests
- Switch traffic from blue to green
- Keep blue as rollback option
Shadow Mode
Test new models with production data:
- New model receives copies of requests
- Predictions logged but not returned
- Compare with current model
- Validate before full deployment
Conclusion
Successful ML in production requires treating models as software: version control, testing, monitoring, and deployment automation. Invest in MLOps infrastructure early to avoid painful migrations later.
Contact us to discuss your ML infrastructure needs.