What is data / model lineage and why does it matter?

medium

Answer

$Lineage = trace of every data + code + config + upstream dependency that produced an artifact.$
$Model lineage: dataset version \to feature spec \to training code commit \to hyperparameters \to resulting weights \to deployed endpoint.$
$Tools: MLflow, Weights & Biases, Neptune, Kubeflow Metadata.$
$Matters for: (1) reproducibility, (2) blame during incidents, (3) regulatory compliance (GDPR / SR 11-7 / EU AI Act), (4) impact analysis when data source changes.$

Check yourself — multiple choice

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Trace of every input (data + code + config) that produced artifact; MLflow / W&B / Neptune / Kubeflow Metadata; reproducibility + blame + compliance + impact analysis
Same as backup
Not real

Lineage: full provenance trace; reproduce / blame / compliance / impact.

#reproducibility#mlops

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