MLOps is to machine learning what DevOps is to software engineering: the set of practices that make deployment reliable, repeatable, and scalable. The difference is that ML models degrade over time in ways that software doesn't — making monitoring and retraining essential, not optional.
The MLOps Pipeline
A production MLOps pipeline: (1) Data validation — check input data for schema changes and quality. (2) Training — automated retraining when performance drops. (3) Evaluation — automated quality, bias, and latency checks. (4) Registry — version every model with metadata. (5) Deployment — canary or blue-green rollout. (6) Monitoring — track prediction quality, drift, and latency in production.
Model Registry: The Source of Truth
Every model in production should be registered with: version number, training data hash, evaluation metrics, owner, and deployment status. When something goes wrong, the registry tells you exactly which model version is running, what data it was trained on, and who to contact.
Monitoring: Beyond Uptime
Software monitoring checks if the system is up. ML monitoring must also check: is the model still accurate? Has the input data distribution shifted? Are predictions biased? Is latency increasing? These ML-specific metrics need dedicated dashboards and alerting — not just infrastructure monitoring.
Automated Retraining
Models degrade. The question is when to retrain: on a schedule (weekly/monthly), when accuracy drops below a threshold, or when data drift is detected. Automated retraining with evaluation gates — only deploy the new model if it's better than the current one — is the gold standard.
核心要点
- The MLOps Pipeline
- Model Registry: The Source of Truth
- Monitoring: Beyond Uptime
- Automated Retraining
总结
Models degrade. The question is when to retrain: on a schedule (weekly/monthly), when accuracy drops below a threshold, or when data drift is detected. Automated retraining with evaluation gates — onl...
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