MLOps is the set of practices that makes ML deployment repeatable. It's how you move from notebooks to systems you can deploy, monitor, and improve without guesswork.
Reproducibility
- Pin environments and dependencies
- Version data and features (not just code)
- Track training configs and seeds
CI/CD for ML
- Unit tests for feature code and data transforms
- Data validation checks (schema, ranges, null rates)
- Model registry + rollout strategy (canary, shadow, rollback)
Monitoring in Production
- System: latency, throughput, error rates
- Data: drift, outliers, missingness changes
- Quality: business-aligned metrics, feedback loops
Governance and Safety
- Access control for data and model artifacts
- Audit trails for training and deployments
- Clear ownership and incident response
Where to start: Add experiment tracking + data validation first. Those two changes alone make teams much faster.