---
title: "Model registry | Skills We Assign For | Azendo"
description: "Model registries — versioning, lineage and promotion, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/model-registry/"
---

[Skills](https://azendo.co/skills/) MLOps and model delivery 

# Model registry.

A model registry is the catalogue of trained models: versions, the data and code that produced each, evaluation results, stage — staging, production, archived — and who approved the promotion.

## Where Model registry fits on a long engagement.

The question a registry answers is the one that matters after an incident: which model is in production, what produced it, and how did it score. Without one, the answer is reconstructed from memory and file names, and it is frequently wrong.

Promotion should be a governed transition rather than a file copy. A model moving to production ought to carry its evaluation results, an approver and a timestamp, because that record is what makes a rollback decision possible when a deployed model starts behaving differently.

## What an assigned team does with Model registry.

Lineage is the part that is hardest to retrofit. Linking a model version to the exact training data, code commit and hyperparameters has to be captured at training time; afterwards it is genuinely unrecoverable.

Building that in from the first training run is far cheaper than adding it later, and it is part of the delivery standard scoped under [ai engineering services](https://azendo.co/services/ai-engineering/).

## What we use Model registry for.

* Knowing what is actually deployed A definitive record of the production model and what produced it.
* Rollback to a known-good version Previous models retained with their scores, so reverting is a decision rather than a rebuild.
* Promotion with an approver A governed transition carrying evaluation results and accountability.

## How Model registry capacity is assigned.

Model governance is assigned inside AI capacity, with lineage captured at training time because it cannot be reconstructed afterwards.

## Roles we assign Model registry for

* [MLOps Engineer MLOps engineering](https://azendo.co/services/mlops-engineering/mlops-engineer/)

## Service lines it sits in

* [MLOps engineering](https://azendo.co/services/mlops-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in mlops and model delivery

* [ONNX — skill we assign for](https://azendo.co/skills/onnx/)
* [TensorRT — skill we assign for](https://azendo.co/skills/tensorrt/)
* [Weights & Biases — skill we assign for](https://azendo.co/skills/weights-and-biases/)
* [Comet — skill we assign for](https://azendo.co/skills/comet/)
* [Neptune.ai — skill we assign for](https://azendo.co/skills/neptune-ai/)
* [DVC — skill we assign for](https://azendo.co/skills/dvc/)
* [Feast — skill we assign for](https://azendo.co/skills/feast/)
* [feature stores — skill we assign for](https://azendo.co/skills/feature-stores/)

## Tell us what your roadmap needs Model registry for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)