What does CI/CD look like for ML?
mediumAnswer
- CI: on code and data changes, run unit tests, data validation (schema, ranges), training smoke tests, and model quality tests (evaluate on frozen validation set, check for regression).
- CD: package model artifact, register in the model registry, deploy to staging, run integration and load tests, then canary/shadow to production.
- Include DVC or MLflow for versioning of data and experiments.
Check yourself — multiple choice
- CI for ML is identical to CI for regular software with no ML-specific tests
- CI for ML includes data validation and model quality tests
- Model registry is only for logging metrics
- Data versioning is unnecessary
ML CI adds data schema, drift, and evaluation tests to the pipeline.
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