Deploy, monitor, and govern ML systems
Plan for changing data, uneven impacts, privacy, and human review after launch.
- Identify what to monitor and when a deployed model needs review or retraining.
A deployed model operates in a changing environment. Monitor input quality, data or concept drift, prediction patterns, error rates when labels become available, latency, and relevant subgroup outcomes. Establish owners, thresholds, and a response plan before launch. Protect personal data, document limitations, and provide human review or appeal where model outputs affect important decisions.
A small example
1baseline_error = 0.08
2current_error = 0.14
3threshold = 0.12
4if current_error > threshold:
5 print("Investigate model performance")Investigate model performance
A performance change does not automatically mean retraining is the right fix; investigate data pipelines, user behavior, policy changes, and measurement quality. Retraining requires renewed evaluation and deployment checks. Keep a rollback plan and make it possible to disable a model if it causes harm.
Key takeaways
Identify what to monitor and when a deployed model needs review or retraining.
Evaluate on relevant unseen data and monitor the system after deployment.
Lesson quiz
5 questions · pass with 4 correct · up to 50 XP
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