MLflow model registry — what does it provide?

medium

Answer

$Centralized store for model artifacts + metadata.$
$Features: (1) staging \to production lifecycle.$
$(2) version history.$
$(3) approval workflow.$
$(4) tags (champion / challenger).$
$(5) model signature (inputs + outputs schema).$
$(6) linked run + git commit + data version.$
$Serves as source of truth for 'what's deployed'.$
$Alternatives: BentoML, Vertex AI Model Registry, SageMaker Model Registry, Weights & Biases Artifacts.$

Check yourself — multiple choice

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Central store for model artifacts + metadata; staging→prod lifecycle + versions + approval + tags + schema + lineage; source of truth for deployment
Just storage
Not real

Model registry: staging lifecycle + versions + approval + lineage.

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