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Always know exactly which model is running in production — and why.

Model Registry

Provides a central model catalog with versioning, stage transitions (staging → production → archived), access controls, and approval workflows for every model across the organization. Creates the single source of truth for model assets that prevents 'which model is actually running in production' confusion common in growing ML teams.

Why It Matters

ModelRegistry answers a question that should be simple but rarely is — which model version is actually running in production, who approved it, and why.
A central catalog with staged transitions and approval workflows makes the organization’s models managed assets instead of scattered artifacts nobody fully accounts for.
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Ends "which model is live" confusion

As ML teams grow, model versions multiply across notebooks, buckets, and endpoints — a single source of truth eliminates the guesswork that causes wrong-model incidents.

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Governed transitions, not silent swaps

Staging-to-production promotion requires explicit approval workflows — every production model has a documented owner, approver, and rationale on record.

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Multi-team sharing with real access controls

Governance controls absent from basic MLflow Registry let teams reuse each other's models safely — sharing without losing accountability.

The Cloudly Advantage

ModelRegistry is the organizing spine of the entire Cloudly platform — the catalog everything else reads from and writes to: ModelCI validates registry artifacts, CanaryShield promotes them, RetrainBot registers new versions, ExplainabilityLayer explains them.
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The platform's connective spine

Every Cloudly product transacts through the registry — selling it first makes each subsequent product a natural plug-in, and selling it last ties everything already bought together.

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Governance suite completion

Approval workflows and stage controls add the model-asset layer to our lineage, notebook, and explainability governance — the full audit story regulators expect, end to end.

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Beyond-MLflow differentiation

"Multi-team governance that basic MLflow Registry lacks" gives BD a concrete upgrade pitch to the many enterprises already running vanilla MLflow.

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Audit-committee entry point

"Can you prove which model made this decision and who approved deploying it?" is a question every BFSI risk committee must answer — often our fastest door-opener.

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Low-cost, high-attach landing product

Lightweight to deploy, immediately valuable, and naturally expands into ModelCI, CanaryShield, and the full lifecycle platform.

The Final Takeaway

SageMaker and W\&B integration services — Registry integration across MLflow, SageMaker, and Weights & Biases footprints generates consultancy work in whichever stack the client already owns.

Powered By

MLflow Model Registry

Core to the ModelRegistry technology stack.

AWS SageMaker Model Registry

Core to the ModelRegistry technology stack.

Weights & Biases Registry

Core to the ModelRegistry technology stack.

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Core to the ModelRegistry technology stack.

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