Design an Automated Model Retraining Pipeline — System Design Interview Practice
Design a system that automatically retrains ML models when performance degrades or new data arrives, validates retrained models against quality gates, and deploys approved models to production. Work through the requirements, architecture trade-offs, and an interactive design review.
Requirements and concepts to consider
- Trigger retraining on schedule or drift detectionRequirement
- Automatically collect and validate training dataRequirement
- Train models with consistent configurationsRequirement
- Evaluate retrained models against baselinesRequirement
- Implement quality gates for deployment approvalRequirement
- Roll back to previous model on validation failureRequirement
- Track all retraining events and outcomesRequirement
- Support both scheduled and event-driven retrainingRequirement
- mlopsConcept to explore
- continuous trainingConcept to explore
- retrainingConcept to explore
- automationConcept to explore
- ci cdConcept to explore
- pipelineConcept to explore