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A model registry catalogs named model versions and associated artifacts or metadata.
Déglul tegtal biiIA-nettali · ~2 min
It helps teams locate evaluated versions and deployment references, while actual rollout, recovery, and reproducibility depend on the surrounding workflow.
A model registry organizes versions that might otherwise remain scattered across files and experiment runs. A registered version can reference its model artifact and related run metadata, while tags or aliases provide named references and status information. Current MLflow registry workflows use aliases and tags; fixed model stages are deprecated. Other platforms can use different lifecycle conventions. A registry helps locate previous artifacts for rollback, but serving must actually load and validate the selected version. Recorded lineage supports investigation and attempted reproduction without guaranteeing identical results. Approval checks, deployment automation, and access controls need to be configured by the team.
A team evaluates a registered model version and updates a deployment alias after approval.
An on-call engineer uses a previous version as a rollback candidate and validates that the serving system loads it correctly.
A reviewer examines the recorded dataset, code, environment, and evaluation evidence for a registered model.
An MLOps team links model versions with experiment results before selecting a deployment candidate.
Teams can use registries to organize model artifacts alongside evaluation and deployment evidence. Generative-AI applications may also need to record adapters, prompts, and the application configuration used for a release. Linking monitoring and rollback workflows can improve traceability, but a registry record alone does not establish regulatory compliance or current serving state. Useful practice is to verify both the recorded candidate and the artifact actually loaded by the application.
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A model registry catalogs named model versions and associated artifacts or metadata. It helps teams locate evaluated versions and deployment references, while actual rollout, recovery, and reproducibility depend on the surrounding workflow.
Registre dafay topp xeetu model yi, seeni lignée ak seeni metrics, ak seen etape deployment, suko defee ku nekk xam ban model mooy dundu ak ni ñu ko tabaxee.
Aliases identify registered versions. Fixed MLflow model stages are deprecated, and other registries can use different lifecycle conventions.
Retained artifacts make recovery possible, but the deployment workflow must load the chosen version and check its behavior.
Siñaale bi dafay leeral li ñuy seentu ci duggal ak genn suko defee sistem yiy serwiis mëna xool laaj yi ak jàpp lu jaarul yoon balaa ñuy indi njuumte yu noppi.
Lineage dafay may ekip yi ñu tabaxaat bépp version ba noppi may auditeur yi ñu firndeel ndànk ban done ak kod moo defar model biñ dugal.
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