CI/CD for Machine Learning
Why CI/CD for ML?
In software engineering, CI/CD ensures code changes are tested and deployed automatically. ML CI/CD extends this to model changes: CI for ML: • Code linting and unit tests • Data validation (schema, statistics, nulls) • Model training run • Model evaluation vs baseline • Performance regression check CD for ML: • Model registry push (if metrics pass) • Canary deployment (route 5% of traffic) • Shadow mode testing (run new model, don't serve output) • A/B testing rollout
Model Evaluation Gate
Before deploying a new model, compare it against the current production model (champion): • New model must beat champion on held-out test set • Must pass fairness checks (no significant performance gap across subgroups) • Must pass latency budget (p99 < 100ms) • Must pass minimum sample size for statistical significance Only if all gates pass does the model go to production.
GitHub Actions ML Pipeline
Use DVC (Data Version Control) alongside Git to version datasets and model artifacts — so any experiment can be reproduced exactly.
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