MLOps
Table of contents
- Purpose
- Model lifecycle
- Monitoring
- Drift detection
- Validation reports
- Retraining governance
- Release and rollback
- Related guides
Purpose
MLOps provides repeatable evaluation, monitoring, release, and governance for RetainAI analytical capabilities.
Model lifecycle
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data version
training
evaluation
approval
registration
deployment
monitoring
review
retirementMonitoring
v0.5 should establish:
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data quality monitoring
prediction distribution monitoring
model-performance monitoring
service health
quota and error visibilityDrift detection
Distinguish:
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input drift
prediction drift
target drift
performance degradation
concept driftA drift alert is evidence for investigation, not automatic proof that a model must be replaced.
Validation reports
Dashboard reports should summarize:
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dataset version
model version
evaluation window
quality checks
performance metrics
drift status
known limitationsRetraining governance
Automated retraining requires:
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versioned datasets
reproducible pipelines
approval gates
evaluation thresholds
model registry
rollback
audit trailRetraining should not deploy automatically solely because drift was detected.
Release and rollback
Application deployment and infrastructure deployment remain separate.
Every release should have an immutable identifier and a documented rollback path.