Key differences between traditional and ML CI/CD pipelines?
WHAT IT TESTS: ML CI/CD manages data and model lineage, not just code. ANSWER: Contrast code deploys with data versioning, model registries, and retraining; note holdout eval. RED FLAG: Treating the model as a static binary ignoring data or retraining context.
WHAT IT TESTS: Whether you recognize that ML CI/CD extends beyond code to manage data and model lineage. ANSWER OUTLINE: First, contrast immutable code deploys with training pipelines that consume versioned datasets and produce stochastic outputs; second, name unique artifacts like serialized model files, feature stores, and experiment metadata; third, explain retraining triggers, A/B gates, and production drift monitoring.
Read the original → docs.cloud.google.com
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- #ci/cd
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